1244 Inference Jobs - Page 8

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,devops,bash scripting,pulumi,go,kubernetes,docker,ansible,terraform,eks,iac,version control,gke,python,ml infrastructure,automation tools,aks

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

About The Opportunity We’re a deep-tech innovator at the intersection of Artificial Intelligence, machine-learning infrastructure, and edge-to-cloud platforms . Our award-winning solutions let Fortune-500 enterprises build, train, and deploy large-scale AI models—seamlessly, securely, and at lightning speed. As global demand for generative AI, RAG pipelines, and autonomous agents accelerates, we’re scaling our MLOps team to keep our customers two steps ahead of the curve. Role & Responsibilities (max 6) Own the full MLOps stack—design, build, and harden GPU-accelerated Kubernetes clusters across on-prem DCs and AWS/GCP/Azure for model training, fine-tuning, and low-latency inference. Automate everything: craft IaC modules (Terraform/Pulumi) and CI/CD pipelines that deliver zero-downtime releases and reproducible experiment tracking. Ship production-grade LLM workloads—optimize RAG/agent pipelines, manage model registries, and implement self-healing workflow orchestration with Kubeflow/Flyte/Prefect. Eliminate bottlenecks: profile CUDA, resolve driver mismatches, and tune distributed frameworks (Ray, DeepSpeed) for multi-node scale-out. Champion reliability: architect HA data lakes, databases, ingress/egress, DNS, and end-to-end observability (Prometheus/Grafana) targeting 99.99 % uptime. Mentor & influence: instill platform-first mind-set, codify best practices, and report progress/road-blocks directly to senior leadership. Skills & Qualifications (max 6) Must-Have 5 + yrs DevOps/Platform experience with Docker & Kubernetes; expert bash/Python/Go scripting. Hands-on building ML infrastructure for distributed GPU training and scalable model serving. Deep fluency in cloud services (EKS/GKE/AKS), networking, load-balancing, RBAC, and Git-based CI/CD. Proven mastery of IaC & config-management (Terraform, Pulumi, Ansible). Preferred Production experience with LLM fine-tuning, RAG architectures, or agentic workflows at scale. Exposure to Kubeflow, Flyte, Prefect, or Ray; track record of setting up observability and data-lake pipelines (Delta Lake, Iceberg). Skills: cloud services,containerization,automation tools,version control,devops

Join Tether and Shape the Future of Digital Finance At Tether, we’re not just building products, we’re pioneering a global financial revolution. Our cutting-edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction. Innovate with Tether Tether Finance: Our innovative product suite features the world’s most trusted stablecoin, USDT , relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services. But that’s just the beginning: Tether Power: Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities. Tether Data: Fueling breakthroughs in AI and peer-to-peer technology, we reduce infrastructure costs and enhance global communications with cutting-edge solutions like KEET , our flagship app that redefines secure and private data sharing. Tether Education : Democratizing access to top-tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity. Tether Evolution : At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways. Why Join Us? Our team is a global talent powerhouse, working remotely from every corner of the world. If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We’ve grown fast, stayed lean, and secured our place as a leader in the industry. If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you. Are you ready to be part of the future? About the job: As a member of our AI model team, you will drive innovation in model serving and inference architectures for advanced AI systems. Your work will focus on optimizing model deployment and inference strategies to deliver highly responsive, efficient, and scalable performance across real-world applications. You will work on a wide spectrum of systems, ranging from resource-efficient models designed for limited hardware environments to complex, multi-modal architectures that integrate data such as text, images, and audio. We expect you to have deep expertise in designing and optimizing model serving pipelines and inference frameworks as well as a strong background in advanced model architectures. You will adopt a hands-on, research-driven approach to develop, test, and implement novel serving strategies and inference algorithms. Your responsibilities include engineering robust inference pipelines, establishing comprehensive performance metrics, and identifying and resolving bottlenecks in production environments. The ultimate goal is to enable high-throughput, low-latency, low-memory footprint, and scalable AI performance that delivers tangible value in dynamic, real-world scenarios. Responsibilities: Design and deploy state-of-the-art model serving architectures that deliver high throughput and low latency while optimizing memory usage. Ensure these pipelines run efficiently across diverse environments, including resource-constrained devices and edge platforms. Establish clear performance targets such as reduced latency, improved token response, and minimized memory footprint. Build, run, and monitor controlled inference tests in both simulated and live production environments. Track key performance indicators such as response latency, throughput, memory consumption, and error rates, with special attention to metrics specific to resource-constrained devices. Document iterative results and compare outcomes against established benchmarks to validate performance across platforms. Identify and prepare high-quality test datasets and simulation scenarios tailored to real-world deployment challenges, specifically those encountered on low-resource devices. Set measurable criteria to ensure that these resources effectively evaluate model performance, latency, and memory utilization under various operational conditions. Analyze computational efficiency and diagnose bottlenecks in the serving pipeline by monitoring both processing and memory metrics. Address issues such as suboptimal batch processing, network delays, and high memory usage to optimize the serving infrastructure for scalability and reliability on resource-constrained systems. Work closely with cross-functional teams to integrate optimized serving and inference frameworks into production pipelines designed for edge and on-device applications. Define clear success metrics such as improved real-world performance, low error rates, robust scalability, optimal memory usage and ensure continuous monitoring and iterative refinements for sustained improvements. Job requirements: A degree in Computer Science or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences). Proven experience in low-level kernel optimizations and inference optimization on mobile devices is essential. Your contributions should have led to measurable improvements in inference latency, throughput, and memory footprint for domain-specific applications, particularly on resource-constrained devices and edge platforms. A deep understanding of modern model serving architectures and inference optimization techniques is required. This includes state-of-the-art methods for achieving low-latency, high-throughput performance, and efficient memory management in diverse, resource-constrained deployment scenarios. Must have strong expertise in writing CPU and GPU kernels for mobile devices (i.e., smartphones) as well as a deep understanding of model serving frameworks and engines. Practical experience in developing and deploying end-to-end inference pipelines, from optimizing models for efficient serving to integrating these solutions on resource-constrained devices is required. Demonstrated ability to apply empirical research to overcome challenges in model serving, such as latency optimization, computational bottlenecks, and memory constraints. You should be proficient in designing robust evaluation frameworks and iterating on optimization strategies to continuously push the boundaries of inference performance and system efficiency.

Introduction IBM Research takes responsibility for technology and its role in society. Working in IBM Research means you'll join a team who invent what's next in computing, always choosing the big, urgent and mind-bending work that endures and shapes generations. Our passion for discovery, and excitement for defining the future of tech, is what builds our strong culture around solving problems for clients and seeing the real world impact that you can make. IBM's product and technology landscape includes Research, Software, and Infrastructure. Entering this domain positions you at the heart of IBM, where growth and innovation thrive. Your Role And Responsibilities Research Scientist position at IBM India Research Lab is a challenging, dynamic and highly innovative role, where you will be responsible for coming up with new innovative ideas, developing solutions working as a team, building prototypes, publishing research papers and demonstrating the value of your ideas in an enterprise setting. Some of our current areas of work where we are actively looking for top researchers are: Optimized runtime stacks for foundation model workloads including fine-tuning, inference serving and large-scale data engineering, with a focus on multi-stage tuning including reinforcement learning, inference-time compute, and data preparation needs for complex AI systems. Optimizing models to run on multiple accelerators including IBM’s AIU accelerator leveraging compiler optimizations, specialized kernels, libraries and tools. Innovative use cases that effectively leverage the infrastructure and models to deliver value Pre-training language and multi-modal foundation models working with large scale distributed training procedures, model alignment, and creating specialized pipelines for various tasks including effective LLM-generated data pipelines. Required Technical And Professional Expertise You should have one or more of the following: A master’s degree in computer science, AI or related fields from a top institution 0-8 years of experience working with modern ML techniques including but not limited to model architectures, data processing, fine-tuning techniques, reinforcement learning, distributed training, inference optimizations Experience with big data platforms like Ray and Spark Experience working with Pytorch FSDP and HuggingFace libraries Programming experience in one of the following: Python, web development technologies Growth mindset and a pragmatic attitude Preferred Technical And Professional Experience Peer-reviewed research at top machine learning or systems conferences Experience working with pytorch.compile, CUDA, triton kernels, GPU scheduling, memory management Experience working with open-source communities

When you join Verizon You want more out of a career. A place to share your ideas freely — even if they’re daring or different. Where the true you can learn, grow, and thrive. At Verizon, we power and empower how people live, work and play by connecting them to what brings them joy. We do what we love — driving innovation, creativity, and impact in the world. Our V Team is a community of people who anticipate, lead, and believe that listening is where learning begins. In crisis and in celebration, we come together — lifting our communities and building trust in how we show up, everywhere & always. Want in? Join the #VTeamLife. What You Will Be Doing... The Commercial Data & Analytics - Impact Analytics team is part of the Verizon Global Services (VGS) organization.The Impact Analytics team addresses high-impact, analytically driven projects focused within three core pillars: Customer Experience, Pricing & Monetization, Network & Sustainability. In this role, you will analyze large data sets to draw insights and solutions to help drive actionable business decisions. You will also apply advanced analytical techniques and algorithms to help us solve some of Verizon’s most pressing challenges. Use your analysis of large structured and unstructured datasets to draw meaningful and actionable insights Envision and test for corner cases. Build analytical solutions and models by manipulating large data sets and integrating diverse data sources Present the results and recommendations of statistical modeling and data analysis to management and other stakeholders Leading the development and implementation of advanced reports and dashboard solutions to support business objectives. Identify data sources and apply your knowledge of data structures, organization, transformation, and aggregation techniques to prepare data for in-depth analysis Deeply understand business requirements and translate them into well-defined analytical problems, identifying the most appropriate statistical techniques to deliver impactful solutions. Assist in building data views from disparate data sources which powers insights and business cases Apply statistical modeling techniques / ML to data and perform root cause analysis and forecasting Develop and implement rigorous frameworks for effective base management. Collaborate with cross-functional teams to discover the most appropriate data sources, fields which caters to the business needs Design modular, reusable Python scripts to automate data processing Clearly and effectively communicate complex statistical concepts and model results to both technical and non-technical audiences, translating your findings into actionable insights for stakeholders. What we’re looking for… You have strong analytical skills, and are eager to work in a collaborative environment with global teams to drive ML applications in business problems, develop end to end analytical solutions and communicate insights and findings to leadership. You work independently and are always willing to learn new technologies. You thrive in a dynamic environment and are able to interact with various partners and cross functional teams to implement data science driven business solutions. You Will Need To Have Bachelor’s degree or six or more years of work experience Six or more years of relevant work experience Experience in managing a team of data scientists that supports a business function. Proficiency in SQL, including writing queries for reporting, analysis and extraction of data from big data systems (Google Cloud Platform, Teradata, Spark, Splunk etc) Curiosity to dive deep into data inconsistencies and perform root cause analysis Programming experience in Python (Pandas, NumPy, Scipy and Scikit-Learn) Experience with Visualization tools matplotlib, seaborn, tableau, grafana etc. A deep understanding of various machine learning algorithms and techniques, including supervised and unsupervised learning Understanding of time series modeling and forecasting techniques Even better if you have one or more of the following: Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and deploying machine learning models at scale using platforms like Domino Data Lab or Vertex AI Experience in applying statistical ideas and methods to data sets to answer business problems. Ability to collaborate effectively across teams for data discovery and validation Experience in deep learning, recommendation systems, conversational systems, information retrieval, computer vision Expertise in advanced statistical modeling techniques, such as Bayesian inference or causal inference. Excellent interpersonal, verbal and written communication skills. Where you’ll be working In this hybrid role, you'll have a defined work location that includes work from home and assigned office days set by your manager. Scheduled Weekly Hours 40 Equal Employment Opportunity Verizon is an equal opportunity employer. We evaluate qualified applicants without regard to race, gender, disability or any other legally protected characteristics.

Our Company Changing the world through digital experiences is what Adobe’s all about. We give everyone—from emerging artists to global brands—everything they need to design and deliver exceptional digital experiences! We’re passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen. We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours! About The Team The Adobe Express team is building a path-breaking, all-in-one creative application for all platforms - web, desktop, and mobile. This platform combines the power of Adobe’s creative technologies with intuitive, AI-enhanced workflows that empower anyone to create standout content quickly and effortlessly. The Opportunity We’re looking for a Senior Machine Learning Engineer (Architect) to play a key role in shaping the next generation of AI-powered creative experiences in Adobe Express. This role will build high-impact AI Workflows for Adobe Express in Image editing space. Concentrating on brand new generative AI workflows, including personalized assistants and intelligent creative tools. Speed up AI culture by sharing knowledge, encouraging experimentation, and improving developer efficiency. Experience Requirements: 15+ years of proven experience in hands-on Machine Learning work. As a Machine Learning Engineer, you will: Build and scale advanced ML models to make Image editing easy and seamless for users Develop AI Agents for Adobe Express in Imaging space Partner closely with product, design and engineering teams across Adobe to integrate Adobe’s latest generative AI capabilities user-facing features. Help drive a culture of AI innovation and learning through internal knowledge-sharing, best-practice documentation, and experimentation frameworks that boost team productivity. Detailed Responsibilities: Research, design, and implement advanced ML models and scalable pipelines across training, inference, and deployment stages, using techniques in computer vision, NLP, deep learning, and generative AI. Integrate Large Language Models (LLMs) and agent-based frameworks to support multimodal creative workflows—enabling rich, context-aware, dynamic user experiences. Design, implement, and optimize subagent architectures for supporting modular and intelligent assistance across various creative tasks in Adobe Express. Collaborate with multi-functional teams to translate product requirements into ML solutions—especially those related to Harmony GenAI, smart recommendations, and generative tooling. Contribute to the development of internal platforms for model experimentation, A/B testing, performance monitoring, and continuous improvement. Stay up-to-date with evolving ML/GenAI research, tools, and frameworks—including federated learning, retrieval-augmented generation, and optimization for real-time inference. Champion an AI-first culture by mentoring peers, promoting learning opportunities, and encouraging innovation at both technical and organizational levels. Special Skills Requirements: Proficiency in Python for model development and C++ for systems integration. Strong hands-on experience with TensorFlow, PyTorch, and emerging GenAI toolkits. Experience working with LLMs, agent architectures, and user interfaces powered by AI technology. Deep understanding of computer vision and NLP techniques, especially for multimodal AI applications. Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other applicable characteristics protected by law. Learn more about our vision here. Adobe aims to make Adobe.com accessible to any and all users. If you have a disability or special need that requires accommodation to navigate our website or complete the application process, email accommodations@adobe.com or call (408) 536-3015.

Join us as we work to create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all. This position requires expertise in designing, developing, debugging, and maintaining AI-powered applications and data engineering workflows for both local and cloud environments. The role involves working on large-scale projects, optimizing AI/ML pipelines, and ensuring scalable data infrastructure. As a PMTS, you will be responsible for integrating Generative AI (GenAI) capabilities, building data pipelines for AI model training, and deploying scalable AI-powered microservices. You will collaborate with AI/ML, Data Engineering, DevOps, and Product teams to deliver impactful solutions that enhance our products and services. Additionally, it would be desirable if the candidate has experience in retrieval-augmented generation (RAG), fine-tuning pre-trained LLMs, AI model evaluation, data pipeline automation, and optimizing cloud-based AI deployments. Responsibilities AI-Powered Software Development & API Integration Develop AI-driven applications, microservices, and automation workflows using FastAPI, Flask, or Django, ensuring cloud-native deployment and performance optimization. Integrate OpenAI APIs (GPT models, Embeddings, Function Calling) and Retrieval-Augmented Generation (RAG) techniques to enhance AI-powered document retrieval, classification, and decision-making. Data Engineering & AI Model Performance Optimization Design, build, and optimize scalable data pipelines for AI/ML workflows using Pandas, PySpark, and Dask, integrating data sources such as Kafka, AWS S3, Azure Data Lake, and Snowflake. Enhance AI model inference efficiency by implementing vector retrieval using FAISS, Pinecone, or ChromaDB, and optimize API latency with tuning techniques (temperature, top-k sampling, max tokens settings). Microservices, APIs & Security Develop scalable RESTful APIs for AI models and data services, ensuring integration with internal and external systems while securing API endpoints using OAuth, JWT, and API Key Authentication. Implement AI-powered logging, observability, and monitoring to track data pipelines, model drift, and inference accuracy, ensuring compliance with AI governance and security best practices. AI & Data Engineering Collaboration Work with AI/ML, Data Engineering, and DevOps teams to optimize AI model deployments, data pipelines, and real-time/batch processing for AI-driven solutions. Engage in Agile ceremonies, backlog refinement, and collaborative problem-solving to scale AI-powered workflows in areas like fraud detection, claims processing, and intelligent automation. Cross-Functional Coordination and Communication Collaborate with Product, UX, and Compliance teams to align AI-powered features with user needs, security policies, and regulatory frameworks (HIPAA, GDPR, SOC2). Ensure seamless integration of structured and unstructured data sources (SQL, NoSQL, vector databases) to improve AI model accuracy and retrieval efficiency. Mentorship & Knowledge Sharing Mentor junior engineers on AI model integration, API development, and scalable data engineering best practices, and conduct knowledge-sharing sessions. Education & Experience Required 12-18 years of experience in software engineering or AI/ML development, preferably in AI-driven solutions. Hands-on experience with Agile development, SDLC, CI/CD pipelines, and AI model deployment lifecycles. Bachelor’s Degree or equivalent in Computer Science, Engineering, Data Science, or a related field. Proficiency in full-stack development with expertise in Python (preferred for AI), Java Experience with structured & unstructured data: SQL (PostgreSQL, MySQL, SQL Server) NoSQL (OpenSearch, Redis, Elasticsearch) Vector Databases (FAISS, Pinecone, ChromaDB) Cloud & AI Infrastructure AWS: Lambda, SageMaker, ECS, S3 Azure: Azure OpenAI, ML Studio GenAI Frameworks & Tools: OpenAI API, Hugging Face Transformers, LangChain, LlamaIndex, AutoGPT, CrewAI. Experience in LLM deployment, retrieval-augmented generation (RAG), and AI search optimization. Proficiency in AI model evaluation (BLEU, ROUGE, BERT Score, cosine similarity) and responsible AI deployment. Strong problem-solving skills, AI ethics awareness, and the ability to collaborate across AI, DevOps, and data engineering teams. Curiosity and eagerness to explore new AI models, tools, and best practices for scalable GenAI adoption. About Athenahealth Here’s our vision: To create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all. What’s unique about our locations? From an historic, 19th century arsenal to a converted, landmark power plant, all of athenahealth’s offices were carefully chosen to represent our innovative spirit and promote the most positive and productive work environment for our teams. Our 10 offices across the United States and India — plus numerous remote employees — all work to modernize the healthcare experience, together. Our Company Culture Might Be Our Best Feature. We don't take ourselves too seriously. But our work? That’s another story. athenahealth develops and implements products and services that support US healthcare: It’s our chance to create healthier futures for ourselves, for our family and friends, for everyone. Our vibrant and talented employees — or athenistas, as we call ourselves — spark the innovation and passion needed to accomplish our goal. We continue to expand our workforce with amazing people who bring diverse backgrounds, experiences, and perspectives at every level, and foster an environment where every athenista feels comfortable bringing their best selves to work. Our size makes a difference, too: We are small enough that your individual contributions will stand out — but large enough to grow your career with our resources and established business stability. Giving back is integral to our culture. Our athenaGives platform strives to support food security, expand access to high-quality healthcare for all, and support STEM education to develop providers and technologists who will provide access to high-quality healthcare for all in the future. As part of the evolution of athenahealth’s Corporate Social Responsibility (CSR) program, we’ve selected nonprofit partners that align with our purpose and let us foster long-term partnerships for charitable giving, employee volunteerism, insight sharing, collaboration, and cross-team engagement. What can we do for you? Along with health and financial benefits, athenistas enjoy perks specific to each location, including commuter support, employee assistance programs, tuition assistance, employee resource groups, and collaborative workspaces — some offices even welcome dogs. In addition to our traditional benefits and perks, we sponsor events throughout the year, including book clubs, external speakers, and hackathons. And we provide athenistas with a company culture based on learning, the support of an engaged team, and an inclusive environment where all employees are valued. We also encourage a better work-life balance for athenistas with our flexibility. While we know in-office collaboration is critical to our vision, we recognize that not all work needs to be done within an office environment, full-time. With consistent communication and digital collaboration tools, athenahealth enables employees to find a balance that feels fulfilling and productive for each individual situation.

When you join Verizon You want more out of a career. A place to share your ideas freely — even if they’re daring or different. Where the true you can learn, grow, and thrive. At Verizon, we power and empower how people live, work and play by connecting them to what brings them joy. We do what we love — driving innovation, creativity, and impact in the world. Our V Team is a community of people who anticipate, lead, and believe that listening is where learning begins. In crisis and in celebration, we come together — lifting our communities and building trust in how we show up, everywhere & always. Want in? Join the #VTeamLife. What You Will Be Doing... The Commercial Data & Analytics - Impact Analytics team is part of the Verizon Global Services (VGS) organization.The Impact Analytics team addresses high-impact, analytically driven projects focused within three core pillars: Customer Experience, Pricing & Monetization, Network & Sustainability. In this role, you will analyze large data sets to draw insights and solutions to help drive actionable business decisions. You will also apply advanced analytical techniques and algorithms to help us solve some of Verizon’s most pressing challenges. Use your analysis of large structured and unstructured datasets to draw meaningful and actionable insights Envision and test for corner cases. Build analytical solutions and models by manipulating large data sets and integrating diverse data sources Present the results and recommendations of statistical modeling and data analysis to management and other stakeholders Leading the development and implementation of advanced reports and dashboard solutions to support business objectives. Identify data sources and apply your knowledge of data structures, organization, transformation, and aggregation techniques to prepare data for in-depth analysis Deeply understand business requirements and translate them into well-defined analytical problems, identifying the most appropriate statistical techniques to deliver impactful solutions. Assist in building data views from disparate data sources which powers insights and business cases Apply statistical modeling techniques / ML to data and perform root cause analysis and forecasting Develop and implement rigorous frameworks for effective base management. Collaborate with cross-functional teams to discover the most appropriate data sources, fields which caters to the business needs Design modular, reusable Python scripts to automate data processing Clearly and effectively communicate complex statistical concepts and model results to both technical and non-technical audiences, translating your findings into actionable insights for stakeholders. What we’re looking for… You have strong analytical skills, and are eager to work in a collaborative environment with global teams to drive ML applications in business problems, develop end to end analytical solutions and communicate insights and findings to leadership. You work independently and are always willing to learn new technologies. You thrive in a dynamic environment and are able to interact with various partners and cross functional teams to implement data science driven business solutions. You Will Need To Have Bachelor’s degree or six or more years of work experience Six or more years of relevant work experience Experience in managing a team of data scientists that supports a business function. Proficiency in SQL, including writing queries for reporting, analysis and extraction of data from big data systems (Google Cloud Platform, Teradata, Spark, Splunk etc) Curiosity to dive deep into data inconsistencies and perform root cause analysis Programming experience in Python (Pandas, NumPy, Scipy and Scikit-Learn) Experience with Visualization tools matplotlib, seaborn, tableau, grafana etc. A deep understanding of various machine learning algorithms and techniques, including supervised and unsupervised learning Understanding of time series modeling and forecasting techniques Even better if you have one or more of the following: Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and deploying machine learning models at scale using platforms like Domino Data Lab or Vertex AI Experience in applying statistical ideas and methods to data sets to answer business problems. Ability to collaborate effectively across teams for data discovery and validation Experience in deep learning, recommendation systems, conversational systems, information retrieval, computer vision Expertise in advanced statistical modeling techniques, such as Bayesian inference or causal inference. Excellent interpersonal, verbal and written communication skills. Where you’ll be working In this hybrid role, you'll have a defined work location that includes work from home and assigned office days set by your manager. Scheduled Weekly Hours 40 Equal Employment Opportunity Verizon is an equal opportunity employer. We evaluate qualified applicants without regard to race, gender, disability or any other legally protected characteristics.

About Us Yubi stands for ubiquitous. But Yubi will also stand for transparency, collaboration, and the power of possibility. From being a disruptor in India’s debt market to marching towards global corporate markets from one product to one holistic product suite with seven products Yubi is the place to unleash potential. Freedom, not fear. Avenues, not roadblocks. Opportunity, not obstacles. About Yubi Yubi, formerly known as CredAvenue, is re-defining global debt markets by freeing the flow of finance between borrowers, lenders, and investors. We are the world's possibility platform for the discovery, investment, fulfillment, and collection of any debt solution. At Yubi, opportunities are plenty and we equip you with tools to seize it. In March 2022, we became India's fastest fintech and most impactful startup to join the unicorn club with a Series B fundraising round of $137 million. In 2020, we began our journey with a vision of transforming and deepening the global institutional debt market through technology. Our two-sided debt marketplace helps institutional and HNI investors find the widest network of corporate borrowers and debt products on one side and helps corporates to discover investors and access debt capital efficiently on the other side. Switching between platforms is easy, which means investors can lend, invest and trade bonds - all in one place. All of our platforms shake up the traditional debt ecosystem and offer new ways of digital finance. Yubi Credit Marketplace - With the largest selection of lenders on one platform, our credit marketplace helps enterprises partner with lenders of their choice for any and all capital requirements. Yubi Invest - Fixed income securities platform for wealth managers & financial advisors to channel client investments in fixed income Financial Services Platform - Designed for financial institutions to manage co-lending partnerships & asset based securitization Spocto - Debt recovery & risk mitigation platform Corpository - Dedicated SaaS solutions platform powered by Decision-grade data, Analytics, Pattern Identifications, Early Warning Signals and Predictions to Lenders, Investors and Business Enterprises So far, we have on-boarded over 17000+ enterprises, 6200+ investors & lenders and have facilitated debt volumes of over INR 1,40,000 crore. Backed by marquee investors like Insight Partners, B Capital Group, Dragoneer, Sequoia Capital, LightSpeed and Lightrock, we are the only-of-its-kind debt platform globally, revolutionizing the segment. At Yubi, People are at the core of the business and our most valuable assets. Yubi is constantly growing, with 1000+ like-minded individuals today, who are changing the way people perceive debt. We are a fun bunch who are highly motivated and driven to create a purposeful impact. Come, join the club to be a part of our epic growth story. About The Role We're looking for a highly skilled, results-driven AI Developer who thrives in fast-paced, high-impact environments. If you are passionate about pushing the boundaries of Computer Vision, OCR, NLP and and Large Language Models (LLMs) and have a strong foundation in building and deploying AI solutions, this role is for you. As a Lead Data Scientist, you will take ownership of designing and implementing state-of-the-art AI products. This role demands deep technical expertise, the ability to work autonomously, and a mindset that embraces complex challenges head-on. Here, you won't just fine-tune pre-trained models—you'll be architecting, optimizing, and scaling AI solutions that power real-world applications. Key Responsibilities Architect, develop, and deploy high-performance AI Solutions for real-world applications. Implement and optimize state-of-the-art LLM , OCR models and frameworks. Fine-tune and integrate LLMs (GPT, LLaMA, Mistral, etc.) to enhance text understanding and automation. Build and optimize end-to-end AI pipelines, ensuring efficient data processing and model deployment. Work closely with engineers to operationalize AI models in production (Docker, FastAPI, TensorRT, ONNX). Enhance GPU performance and model inference efficiency, applying techniques such as quantization and pruning. Stay ahead of industry advancements, continuously experimenting with new AI architectures and training techniques. Work in a highly dynamic, startup-like environment, balancing rapid experimentation with production-grade robustness. What We're Looking For Requirements Required Skills & Qualifications: Proven technical expertise – Strong programming skills in Python, PyTorch, TensorFlow with deep experience in NLP and LLM Hands-on experience in developing, training, and deploying LLM and Agentic workflows Strong background in vector databases, RAG pipelines, and fine-tuning LLMs for document intelligence. Deep understanding of Transformer-based architectures for vision and text processing. Experience working with Hugging Face, OpenCV, TensorRT, and NVIDIA GPUs for model acceleration. Autonomous problem solver – You take initiative, work independently, and drive projects from research to production. Strong experience in scaling AI solutions, including model optimization and deployment on cloud platforms (AWS/GCP/Azure). Thrives in fast-paced environments – You embrace challenges, pivot quickly, and execute effectively. Familiarity with MLOps tools (Docker, FastAPI, Kubernetes) for seamless model deployment. Experience in multi-modal models (Vision + Text). Good to Have Financial background and understanding corporate finance . Contributions to open-source AI projects.

Experience: 3+ yrs Location: Delhi, India (On-site) Job Description Hypervise by Eternal Robotics is a cutting-edge industrial AI platform that enables real-time quality inspection, process control, and defect detection through advanced computer vision and deep learning technologies. We serve sectors such as automotive, apparel manufacturing, pharmaceuticals, and packaging by digitizing production lines and delivering operational efficiency through AI. Role Overview We are seeking a highly skilled and proactive Lead Engineer – Computer Vision to architect and lead the development of AI-powered inspection systems and edge deployments. This is a mission-critical role responsible for delivering robust, production-grade computer vision solutions while leading junior engineers across projects and deployments. The ideal candidate thrives in a fast-paced environment, combines strong technical execution with cross-functional collaboration, and has a passion for solving real-world industrial problems using vision AI. Key Responsibilities 1. Project Management & Technical Leadership Lead and monitor end-to-end execution of CV/AI projects, from requirement gathering to final deployment. Collaborate with cross-functional teams (Product, Hardware, QA, Customer Success) to align project milestones. Regularly update stakeholders and prepare detailed technical and status reports. 2. Client Engagement & Time Management Engage with customers to understand and translate use-case requirements into engineering specifications. Manage expectations on delivery timelines and provide technical demonstrations or updates. Support sales/pre-sales efforts with feasibility analysis, proof-of-concept (PoC) development, and architecture design. 3. CV Pipeline Development & Code Quality Design scalable and reusable CV pipelines using best practices in modular software architecture. Lead code reviews and mentor junior team members to ensure consistency and maintainability. Integrate components including ML models, camera streams, and decision layers. 4. Model Development & Optimization Train, evaluate, and optimize object detection, classification, and segmentation models. Utilize frameworks such as TensorFlow, PyTorch, and OpenCV, with an emphasis on YOLO, DeepStream, and Jetson-compatible models. Implement pre- and post-processing pipelines to address challenging industrial imaging conditions. 5. Testing, QA & Deployment Create test cases and validation protocols to verify system performance against customer specs. Supervise on-site and remote deployments; ensure robust integration of edge devices like Jetson Xavier/Nano and industrial cameras. Provide deployment support including remote debugging, calibration, and performance tuning. 6. Continuous Improvement & Innovation Experiment with state-of-the-art models and libraries to enhance detection accuracy and reduce latency. Identify and act on opportunities to improve system resilience, processing speed, and resource utilization. Contribute to IP generation and internal technical documentation. Key Performance Indicators (KPIs) Model Accuracy: Precision and recall metrics in real production environments System Deployments: Number and success rate of on-time installations Resolution Time: Average TAT for solving deployment or inference issues On-Time Delivery: Project milestone adherence across sprints Quality of Deliverables: Based on code audits, testing coverage, and system stability Customer Feedback: Direct user feedback and CSAT/NPS post-deployment Required Qualifications & Experience Education: Bachelor’s degree in Electronics, Computer Science, or a related field. Advanced degrees or certifications in AI/ML are a plus. Experience: 3+ years of hands-on experience in developing computer vision solutions, ideally in manufacturing, robotics, or industrial automation. Domain Knowledge: Experience with industrial cameras, inspection systems, and edge computing setups is highly preferred. Technical Skills Languages: Python (primary), C++ (desirable) Frameworks/Libraries: OpenCV, TensorFlow, PyTorch, YOLO, DeepStream Edge Computing: Jetson Nano/Xavier, deployment on embedded devices Operating Systems: Linux (Ubuntu preferred), bash scripting Integration: ROS, MQTT, GStreamer, Modbus/TCP/IP DevOps: Git/GitHub, Docker, CI/CD familiarity Tools: VS Code, Jupyter, NVIDIA Nsight, camera SDKs (FLIR, Basler, IDS, etc.) Soft Skills Strong analytical and debugging skills with a detail-oriented mindset Clear and concise communication across technical and non-technical teams Ownership mindset with the ability to lead and mentor junior engineers Comfortable in agile, deadline-driven environments and willing to take initiative Why Join Us? Build real-world AI systems that impact global production lines Work in a cross-disciplinary team of engineers, designers, and domain experts Fast-track your growth in a company at the forefront of AI transformation in manufacturing Access cutting-edge tools, datasets, and continuous learning opportunities

Additional Locations: India-Haryana, Gurgaon Diversity - Innovation - Caring - Global Collaboration - Winning Spirit - High Performance At Boston Scientific, we’ll give you the opportunity to harness all that’s within you by working in teams of diverse and high-performing employees, tackling some of the most important health industry challenges. With access to the latest tools, information and training, we’ll help you in advancing your skills and career. Here, you’ll be supported in progressing – whatever your ambitions. GenAI / AI Platform Architect Join Boston Scientific at the forefront of innovation as we embrace AI to transform healthcare and deliver cutting‑edge solutions. We are seeking an experienced GenAI / AI Platform Architect to define, build, and continuously improve a secure, governable, and enterprise‑grade Generative‑AI platform that underpins copilots, RAG search, intelligent document processing, agentic workflows, and other high‑value use cases. Your responsibilities will include: Own the reference architecture for GenAI: LLM hosting, vector DBs, orchestration layer, real‑time inference, and evaluation pipelines. Design and govern Retrieval‑Augmented Generation (RAG) pipelines—embedding generation, indexing, hybrid retrieval, and prompt assembly—for authoritative, auditable answers. Select and integrate toolchains (LangChain, LangGraph, LlamaIndex, MLflow, Kubeflow, Airflow) and ensure compatibility with cloud GenAI services (Azure OpenAI, Amazon Bedrock, Vertex AI). Implement MLOps / LLMOps: automated CI/CD for model fine‑tuning, evaluation, rollback, and blue‑green deployments; integrate model‑performance monitoring and drift detection. Embed “shift‑left” security and responsible‑AI guardrails—PII redaction, model‑output moderation, lineage logging, bias checks, and policy‑based access controls—working closely with CISO and compliance teams. Optimize cost‑to‑serve through dynamic model routing, context‑window compression, and GPU / Inferentia auto‑scaling; publish charge‑back dashboards for business units. Mentor solution teams on prompt engineering, agentic patterns (ReAct, CrewAI), and multi‑modal model integration (vision, structured data). Establish evaluation frameworks (e.g., LangSmith, custom BLEU/ROUGE/BERT‑Score pipelines, human‑in‑the‑loop) to track relevance, hallucination, toxicity, latency, and carbon footprint. Report KPIs (MTTR for model incidents, adoption growth, cost per 1k tokens) and iterate roadmap in partnership with product, data, and infrastructure leads. Required Qualifications: 10+ years designing cloud‑native platforms or AI/ML systems; 3+ years leading large‑scale GenAI, LLM, or RAG initiatives. Deep knowledge of LLM internals, fine‑tuning, RLHF, and agentic orchestration patterns (ReAct, Chain‑of‑Thought, LangGraph). Proven delivery on vector‑database architectures (Pinecone, Weaviate, FAISS, pgvector, Milvus) and semantic search optimization. Mastery of Python and API engineering; hands‑on with LangChain, LlamaIndex, FastAPI, GraphQL, gRPC. Strong background in security, governance, and observability across distributed AI services (IAM, KMS, audit trails, OpenTelemetry). Preferred Qualifications: Certifications: AWS Certified GenAI Engineer – Bedrock or Microsoft Azure AI Engineer Associate. Experience orchestrating multimodal models (images, video, audio) and streaming inference on edge devices or medical sensors. Published contributions to open‑source GenAI frameworks or white‑papers on responsible‑AI design. Familiarity with FDA or HIPAA compliance for AI solutions in healthcare. Demonstrated ability to influence executive stakeholders and lead cross‑functional tiger teams in a fast‑moving AI market. Requisition ID: 608452 As a leader in medical science for more than 40 years, we are committed to solving the challenges that matter most – united by a deep caring for human life. Our mission to advance science for life is about transforming lives through innovative medical solutions that improve patient lives, create value for our customers, and support our employees and the communities in which we operate. Now more than ever, we have a responsibility to apply those values to everything we do – as a global business and as a global corporate citizen. So, choosing a career with Boston Scientific (NYSE: BSX) isn’t just business, it’s personal. And if you’re a natural problem-solver with the imagination, determination, and spirit to make a meaningful difference to people worldwide, we encourage you to apply and look forward to connecting with you!

- 3+ years of non-internship professional software development experience - 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience - Experience programming with at least one software programming language We are part of the India & Emerging Stores Customer Fulfilment Experience Org. Team's mission is to address unique customer requirements and the increasing associated costs/abuse of returns and rejects for Emerging Stores. Our team implements tech solves that reduce the net cost of concessions/refunds - this includes buyer and seller abuse, costs associated with return/reject transportation, cost of contacts and operations cost at return centers. We have a huge opportunity to create a legacy and our Legacy Statement is to “transform ease and quality of living in India, thereby enabling its potential in the 21st century”. We also believe that we have an additional responsibility to “help Amazon become truly global in its perspective and innovations” by creating global best-in-class products/platforms that can serve our customers worldwide. This is an opportunity to join our mission to build tech solutions that empower sellers to delight the next billion customers. You will be responsible for building new system capabilities grounds up for strategic business initiatives. If you feel excited by the challenge of setting the course for large company wide initiatives, building and launching customer facing products in IN and other emerging markets, this may be the next big career move for you. We are building systems which can scale across multiple marketplaces and are on the state-of-the-art in automated large scale e-commerce business. We are looking for a SDE to deliver capabilities across marketplaces. We operate in a high performance agile ecosystem where SDEs, Product Managers and Principals frequently connect with end customers of our products. Our SDEs stay connected with customers through seller/FC/Deliver Station visits and customer anecdotes. This allows our engineers to significantly influence product roadmap, contribute to PRFAQs and create disproportionate impact through the tech they deliver. We offer Technology leaders a once in a lifetime opportunity to transform billions of lives across the planet through their tech innovation. As an engineer, you will help with the design, implementation, and launch of many key product features. You will get an opportunity to work on the wide range of technologies (including AWS Open Search, Lambda, ECS, SQS, Dynamo DB, Neptune etc.) and apply new technologies for solving customer problems. You will have an influence on defining product features, drive operational excellence, and spearhead the best practices that enable a quality product. You will get to work with highly skilled and motivated engineers who are already contributing to building high-scale and high-available systems. If you are looking for an opportunity to work on world-leading technologies and would like to build creative technology solutions that positively impact hundreds of millions of customers, and relish large ownership and diverse technologies, join our team today! As an engineer you will be responsible for: • Ownership of product/feature end-to-end for all phases from the development to the production. • Ensuring the developed features are scalable and highly available with no quality concerns. • Work closely with senior engineers for refining the design and implementation. • Management and execution against project plans and delivery commitments. • Assist directly and indirectly in the continual hiring and development of technical talent. • Create and execute appropriate quality plans, project plans, test strategies and processes for development activities in concert with business and project management efforts. • Contribute intellectual property through patents. The candidate should be passionate engineer about delivering experiences that delight customers and creating solutions that are robust. He/she should be able to commit and own the deliveries end-to-end. About the team Team: IES NCRC Tech Mission: We own programs to prevent customer abuse for IN & emerging marketplaces. We detect abusive customers for known abuse patterns and apply interventions at different stages of buyer's journey like checkout, pre-fulfillment, shipment and customer contact (customer service). We closely partner with International machine learning team to build ML based solutions for above interventions.​ Vision: Our goal is to automate detection of new abuse patterns and act quickly to minimize financial loss to Amazon. This would act as a deterrent for abusers, while building trust for genuine customers. We use machine learning based models to automate the abuse detection in a scalable & efficient manner. Technologies: The ML models leveraged by the team include a vast variety ranging from regression-based (XgBoost), to deep-learning models (RNN, CNN) and use frameworks like PyTorch, TensorFlow, Keras for training & inference. Productionization of ML models for real-time low-latency high traffic use-cases poses unique challenges, which in turn makes the work exciting. In terms of tech stack, multiple AWS technologies are used, e.g. Sagemaker, ECS, Lambda, ElasticSearch, StepFunctions, AWS Batch, DynamoDB, S3, CDK (for infra), GraphDBs and are open to adopt new technologies as per use-case. 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience Bachelor's degree in computer science or equivalent Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Job Title: Backend Developer ( Node.js + Python ) Experience: 4 to 6 Years Employment Type: Full-Time Work Mode: [On-site] About the Role: We’re looking for a highly motivated Backend Developer with strong hands-on experience in Node.js, Python and a working knowledge of AI/ML technologies . You’ll be part of a cross-functional team building scalable backend services that power intelligent, data-driven applications. Key Responsibilities: Develop and maintain backend services using Node.js (Express/Nest.js) and Python (FastAPI/Django) Build and optimize APIs (REST & GraphQL) for web and mobile products. Collaborate with AI/ML teams to integrate models into backend workflows. Work with data scientists to deploy models and manage inference pipelines. Design backend systems to support AI features such as recommendation engines, NLP, chatbots, or computer vision modules. Write clean, maintainable, and testable code with proper documentation. Ensure the performance, security, and scalability of applications. Manage deployments using Docker , CI/CD , and cloud infrastructure (AWS/GCP/Azure). Monitor and troubleshoot production issues proactively. Required Skills and Qualifications: 4 to 6 years of backend development experience in Node.js and Python. Strong knowledge of databases – PostgreSQL , MongoDB , Redis . Solid understanding of API design , microservices , and system architecture. Experience with AI/ML integrations – using or deploying models built with libraries like TensorFlow , PyTorch , scikit-learn , or OpenAI APIs . Experience with cloud services for model hosting and serving (e.g., AWS ec2, AWS Lambda, SageMaker, GCP Vertex AI). Experience with containerization (Docker) and version control (Git). Exposure to WebSockets , real-time communication , and message queues (Kafka, RabbitMQ). Working knowledge of JWT , OAuth2 , and API security best practices. Nice to Have: Hands-on with LangChain , LLM APIs , or vector databases like Pinecone or Weaviate. Familiarity with prompt engineering or LLM fine-tuning . Exposure to serverless architecture or event-driven systems . Job Types: Full-time, Permanent Pay: ₹500,000.00 - ₹700,000.00 per year Benefits: Paid sick time Paid time off Location Type: In-person Schedule: Monday to Friday Work Location: In person Speak with the employer +91 7880122103

We are looking for a visionary and technically skilled Lead AI Engineer – Machine Learning to spearhead the design, development, and deployment of advanced machine learning solutions. In this leadership role, you will guide a team of AI/ML engineers, contribute hands-on to critical technical challenges, and collaborate with cross-functional teams to deliver impactful AI products. This role is ideal for someone who thrives at the intersection of innovation, engineering rigor, and business value. Key Responsibilities Technical Leadership : Lead the architecture, development, and deployment of machine learning models and AI systems across a range of use cases. Model Development : Design, train, and optimize supervised, unsupervised, and deep learning models using frameworks like PyTorch, TensorFlow, and XGBoost. Mentorship : Coach and mentor a team of ML engineers and data scientists; foster a culture of innovation, ownership, and continuous learning. Project Management : Drive planning, execution, and delivery of AI/ML projects, ensuring alignment with business objectives and technical feasibility. System Design : Architect scalable, secure, and high-performance ML pipelines and services using cloud-native tools and MLOps best practices. Collaboration : Work closely with product managers, data engineers, and DevOps teams to translate business problems into AI-driven solutions. Code Quality & Governance : Establish standards for model quality, reproducibility, documentation, versioning, and monitoring. Innovation : Stay current with research and industry trends in ML/AI, evaluate new tools, and introduce state-of-the-art solutions where applicable. Required Skills and Experience Education : Bachelor’s or Master’s in Computer Science, Machine Learning, Data Science, or related technical field Experience : 6–10+ years of experience in software engineering or AI/ML, with at least 2+ years in a technical leadership role Technical Expertise : o Strong programming skills in Python and experience with ML libraries such as Scikit-learn, TensorFlow, PyTorch, Hugging Face o Deep understanding of ML fundamentals: feature engineering, model evaluation, optimization, and deployment o Proficiency in designing and building data pipelines, real-time processing, and model inference systems o Experience with cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI/CD pipelines o Familiarity with MLOps tools (e.g., MLflow, DVC, Airflow, SageMaker) and vector databases (e.g., FAISS, Pinecone) Preferred Qualifications Hands-on experience with LLMs, RAG pipelines, or generative AI applications Familiarity with agentic AI frameworks (LangChain, CrewAI, AutoGPT) Domain expertise in fintech, healthtech, HR tech, or industrial automation Contributions to open-source AI/ML projects or published research Knowledge of responsible AI practices, explainability (XAI), and model governance Soft Skills Strong leadership and team-building skills Clear and persuasive communication with both technical and non-technical stakeholders Strategic thinker with attention to detail and a bias for action Show more Show less

Introduction As a Data Scientist with Gen AI experience at IBM, you will help transform our clients’ data into tangible business value by analyzing information, communicating outcomes and collaborating on product development. Work with Best in Class open source and visual tools, along with the most flexible and scalable deployment options. Whether it’s investigating patient trends or weather patterns, you will work to solve real world problems for the industries transforming how we live. Your Role And Responsibilities The Data Scientist with Gen AI role is designed for a highly analytical and technically skilled individual who excels in data-driven environments. The candidate should possess a strong background in Python programming, database management, and data science methodologies. This role primarily focuses on leveraging data to drive insights and decision-making. The core responsibilities of the role include a range of data science tasks, such as collecting and cleansing data, exploring, and visualizing insights, and applying statistical and mathematical analysis techniques. It involves developing and implementing machine learning and deep learning models, managing big data infrastructure, and executing data engineering tasks. Additionally, the role requires maintaining codebase integrity through version control and designing, creating, and supporting AI-driven products to deliver impactful AI solutions. Responsibilities Include Collecting and cleansing data from diverse sources for analysis, ensuring high-quality and relevant datasets (structured and unstructured) for effective decision-making. Exploring and visualizing data to uncover insights and trends, using advanced tools and techniques for meaningful data interpretation. Applying statistical and mathematical techniques to analyze data, providing robust analytical foundations for predictive modeling and inference. Developing and implementing machine learning and deep learning models Adaptation of foundation models/LLMs to address specific business challenges. Expertise in ML-Ops / AI-Ops Managing big data infrastructure and carrying out data engineering tasks, ensuring efficient data storage, processing, and retrieval. Utilizing version control for maintaining codebase integrity and collaboration, fostering a collaborative and error-free development environment. Designing, creating, and supporting AI-driven products, focusing on delivering scalable and impactful AI solutions that meet user needs and business objectives. Preferred Education Master's Degree Required Technical And Professional Expertise Minimum four years of experience in IT industry using data science and generative AI skills High proficiency in Python programming, NLP techniques and experience using AI Framework (e.g. Hugging Face) Knowledge of SQL and NoSQL database management. Strong background in data science, statistics, mathematics, and analytical techniques. Expertise in machine learning and deep learning methodologies Working knowledge and application of foundation models in addition to Fine tuning of LLMs. Familiarity with big data technologies and data engineering practices. Experience with version control systems, particularly Git, and proficiency with GitHub for code collaboration and repository management. Are able to report and present results to a non-technical audience. This role is ideal for a candidate who is not only technically proficient in data science and generative AI but also skilled in integrating their analytical work with web technologies, cloud computing, and automation. The ability to communicate effectively, manage projects efficiently, and consider the ethical implications of data usage is crucial for success in this role. Preferred Technical And Professional Experience Hands-on experience in data science for four plus years with minimum of 3 years of experience in deep learning Web development skills, including JavaScript and React, for creating sophisticated, interactive data-driven interfaces. Experience with cloud computing platforms (AWS/Azure/Google/IBM) to leverage advanced cloud-based services and infrastructure. Excellent communication skills, crucial for effective teamwork, stakeholder engagement, and clear presentation of data insights and technical concepts. Project management experience with a focus on agile methodologies, ensuring efficient, adaptive, and collaborative project execution. Awareness and understanding of ethical considerations in data science and AI, ensuring responsible and fair use of data and AI technologies. Show more Show less

While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth. If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi! Role: Associate Architect - Machine Learning Experience: 6 to 9 Years Location: Mumbai/Bangalore/Trivandrum Must Have Skills 6+ years of relevant hands-on technical experience implementing, and developing cloud ML solutions on AWS. Hands-on experience on AWS Machine Learning services. Proven experience using AWS Sagemaker leveraging different types of data sources, Training jobs, real-time and batch Inference, and Processing Jobs. Good Experience developing applications using LLMs with Langchain. Must have experience using GenAI frameworks such as vertexAI, OpenAI, AWS Bedrock. Must have Hands-on experience fine-tuning large language models( LLM) and Generative AI (GAI), specifically LLama2. Must have Hands-on experience working with (Retrieval Augmented Generation) RAG architecture and experience using vector indexing such as Opensearch, Elasticsearch. Strong familiarity with higher-level trends in LLMs and open-source platforms. Should have experience with Deep Learning Concepts. Transformers, BERT, Attention models Prompt Engineering: Engineer prompts and optimize few-shot techniques to enhance LLM's performance on specific tasks, e.g. personalized recommendations. Model Evaluation & Optimization: Evaluate LLM's zero-shot and few-shot capabilities, fine-tuning hyperparameters, ensuring task generalization, and exploring model interpretability for robust web app integration. Response Quality: Collaborate with ML and Integration engineers to leverage LLM's pre-trained potential, delivering contextually appropriate responses in a user-friendly web app. Implement and manage MLOps principles and best practices for Gen AI models Thorough understanding of NLP techniques for text representation and modeling Able to effectively design software architecture as required Experience with at least one of the workflow orchestration tools, Airflow, StepFunctions, SageMaker Pipelines, Kubeflow etc.Knowledge of a variety of machine learning techniques (Supervised/unsupervised etc.) (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks Ability to create end to end solution architecture for model training, deployment and retraining using native AWS services such as Sagemaker, Lambda functions, etc. Ability to collaborate with cross-functional teams such as Developers, QA, Project Managers, and other stakeholders to understand their requirements and implement solutions. Good To Have Skills Experience of working for customers/workloads in the Edtech domain with use cases. Experience with software development If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us ! Show more Show less