400 Cuda Jobs - Page 8

Job Area: Engineering Group, Engineering Group > Software Engineering General Summary: Join the exciting Generative AI team at Qualcomm focused on integrating cutting edge GenAI models on Qualcomm chipsets. The team uses Qualcomm chips’ extensive heterogeneous computing capabilities to allow inference of GenAI models on-device without a need for connection to the cloud. Our inference engine is designed to help developers run neural network models trained in a variety of frameworks on Snapdragon platforms at blazing speeds while still sipping the smallest amount of power. Utilize this power efficient hardware and Software stack to run Large Language Models (LLMs) and Large Vision Models (LVM) at near GPU speeds! Responsibilities: In this role, you will spearhead the development and commercialization of the Qualcomm AI Runtime (QAIRT) SDK on Qualcomm SoCs. As an AI inferencing expert, you'll push the limits of performance from large models. Your mastery in deploying large C/C++ software stacks using best practices will be essential. You'll stay on the cutting edge of GenAI advancements, understanding LLMs/Transformers and the nuances of edge-based GenAI deployment. Most importantly, your passion for the role of edge in AI's evolution will be your driving force. Master’s/Bachelor’s degree in computer science or equivalent.2-4 years of relevant work experience in software development.Strong understanding of Generative AI models – LLM, LVM, LMMs and building blocks (self-attention, cross attention, kv caching etc.) Floating-point, Fixed-point representations and Quantization concepts. Experience with optimizing algorithms for AI hardware accelerators (like CPU/GPU/NPU).Strong in C/C++ programming, Design Patterns and OS concepts. Good scripting skills in Python.Excellent analytical and debugging skills. Good communication skills (verbal, presentation, written). Ability to collaborate across a globally diverse team and multiple interests. Preferred Qualifications Strong understanding of SIMD processor architecture and system design. Proficiency in object-oriented software development and familiarity Familiarity with Linux and Windows environment Strong background in kernel development for SIMD architectures. Familiarity with frameworks like llama.cpp, MLX, and MLC is a plus. Good knowledge of PyTorch, TFLite, and ONNX Runtime is preferred. Experience with parallel computing systems and languages like OpenCL and CUDA is a plus. Minimum Qualifications: Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field. 2+ years of academic or work experience with Programming Language such as C, C++, Java, Python, etc.

Qualification BE (CSE/IT)/ B.TECH(CSE/IT)/ MCA/ ME(CSE/IT)/ M.SC/ M.TECH(CSE/IT) Location Surat Salary range Paid Internship Experience/Seniority level Fresher/Internship Job Time Full Time Requirements / Your Skills Annotate, build, train, evaluate, and fine-tune machine learning and deep learning models for various use cases. Implement data pipelines for preprocessing, augmentation, and transformation of structured and unstructured datasets. Perform exploratory data analysis (EDA), feature engineering, and data visualization. Work on computer vision and image/video processing tasks using industry-standard frameworks. Utilize GPU acceleration (CUDA, cuDNN, TensorRT) for training and optimizing deep learning models. Deploy models on-premise and in cloud environments using containerization (Docker) and orchestration (Kubernetes). Collaborate in an agile development team, contributing to architecture, experimentation, and testing workflows.

Qualification BE (CSE/IT)/ B.TECH(CSE/IT)/ MCA/ ME(CSE/IT)/ M.SC/ M.TECH(CSE/IT) Location Surat Salary range Paid Internship Experience/Seniority level Fresher/Internship Job Time Full Time Requirements / Your Skills Annotate, build, train, evaluate, and fine-tune machine learning and deep learning models for various use cases. Implement data pipelines for preprocessing, augmentation, and transformation of structured and unstructured datasets. Perform exploratory data analysis (EDA), feature engineering, and data visualization. Work on computer vision and image/video processing tasks using industry-standard frameworks. Utilize GPU acceleration (CUDA, cuDNN, TensorRT) for training and optimizing deep learning models. Deploy models on-premise and in cloud environments using containerization (Docker) and orchestration (Kubernetes). Collaborate in an agile development team, contributing to architecture, experimentation, and testing workflows.

ABOUT US: The vision from the start has been to create a state-of-the-art infrastructure of the workplace with the implementation of all the tools for employees and clients makes Bytes Technolab a growth hacker. This has really helped the dev team in adapting to the existing & upcoming technologies & platforms to create top-notch software solutions for businesses, startups, and enterprises. Our core value lies with 100% integrity in communication, workflow, methodology, and flexible collaboration. With the client-first approach, we are offering flexible models of engagement that can help our clients in the best way possible. Bytes Technolab is confident that this approach would help us develop user-centric, applicable, advanced, secure, and scalable software solutions. Our team is fully committed to adding value at every stage of your journey with us, from initial engagement to delivery and beyond. Role Description: 3+ years of professional experience in Machine Learning and Artificial Intelligence. Strong proficiency in Python programming and its libraries for ML and AI (NumPy, Pandas, scikit-learn, etc.). Hands-on experience with ML/AI frameworks like PyTorch, TensorFlow, Keras, Facenet, OpenCV, and other relevant libraries. Proven ability to work with GPU acceleration for deep learning model development and optimization (using CUDA, cuDNN). Strong understanding of neural networks, computer vision, and other AI technologies. Solid experience working with Large Language Models (LLMs) such as GPT, BERT, LLaMA, including fine-tuning, prompt engineering, and embedding-based retrieval (RAG). Working knowledge of Agentic Architectures, including designing and implementing LLM-powered agents with planning, memory, and tool-use capabilities. Familiar with frameworks like LangChain, AutoGPT, BabyAGI, and custom agent orchestration pipelines. Solid problem-solving skills and the ability to translate business requirements into ML/AI/LLM solutions. Experience in deploying ML/AI models on cloud platforms (AWS SageMaker, Azure ML, Google AI Platform). Proficiency in building and managing ETL pipelines, data preprocessing, and feature engineering. Experience with MLOps tools and frameworks such as MLflow, Kubeflow, or TensorFlow Extended (TFX). Expertise in optimizing ML/AI models for performance and scalability across diverse hardware architectures. Experience with Natural Language Processing (NLP) and foundational knowledge of Reinforcement Learning. Familiarity with data versioning tools like DVC or Delta Lake. Skilled in containerization and orchestration tools such as Docker and Kubernetes for scalable deployments. Proficient in model evaluation, A/B testing, and establishing continuous training pipelines. Experience working in Agile/Scrum environments with cross-functional teams. Strong understanding of ethical AI principles, model fairness, and bias mitigation techniques. Familiarity with CI/CD pipelines for machine learning workflows. Ability to effectively communicate complex ML, AI, and LLM/Agentic concepts to both technical and non-technical stakeholders. We are hiring professionals with 3+ years of experience in IT Services. Kindly share your updated CV at freny.darji@bytestechnolab.com

Experience: 3+ years Requirements: Excellent knowledge of computer vision concepts, including but not limited to Image Classification, Object Detection, and Semantic Segmentation, developed using state-of-the-art deep learning algorithms. Hands-on experience developing efficient and real-time convolutional neural network (CNN) models for computer vision tasks. Strong proficiency in at least one of the deep learning frameworks, such as PyTorch , TensorFlow , or Caffe, with the ability to apply them to computer vision problems. Quick prototyping skills in Python and coding and debugging proficiency in C++ . Good communication and collaboration skills to work effectively in a team and communicate complex technical concepts. Qualifications: A Ph.D. degree (including candidates at various stages of their Ph.D., such as thesis submission, thesis submitted, degree awaited, synopsis seminar completed, defense completed) in Deep Learning with hands-on coding skills and a passion for an industrial career will be preferred. Master's or Bachelor's degree with thorough industrial work experience in developing computer vision applications using deep learning. Postgraduates or Undergraduates with a strong academic background in Deep Learning, Computer Vision, or related fields, and demonstrated coding skills, are also encouraged to apply. Preferred: Publications in top-tier computer vision conferences like CVPR, ICCV, ECCV , or major AI conferences like NeurIPS. Knowledge of computer vision libraries and tools, including OpenCV and DLib, and a solid understanding of image processing and computer vision fundamentals. Hands-on experience with model compression and pruning techniques in deep learning. Good exposure to various deep learning architectures, such as Artificial Neural Networks (ANN), Deep Neural Networks (DNN) , Convolutional Neural Networks (CNN) , Recurrent Neural Networks (RNN ), and Long Short-Term Memory (LSTM) networks. Familiarity with GPU programming (e.g., CUDA, OpenCL) for efficient deep-learning computations. Pay is competitive as per market standards.

Job Title : C++ Developer Duration : 1-year contractual position Experience Range : 5 to 8 years Notice Period : Within 20 days Location : Kandivali, Mumbai (Only local candidates of Mumbai are acceptable) Education : B.Tech, B.E Interview Process : 1st- Technical, 2nd - Technical round & 3rd - HR Round Mandatory : End-to-end C++ skills Skills Required : - C, C++ - Qt/QML - OOPs - STL, Data Structures - JavaScript - Automotive Product Development - Android Application Development - Java - API - GitLab CI/CD - GitHub, Gerrit - Jira, Zoho - PostgreSQL, SQLite, JSON - MVVM Architecture - Testing - Debugging - Linux, Unix Job Description : We are seeking an experienced Developer with a strong background in C++, CUDA programming, and Linux to guide our development team in building cutting-edge solutions for device integration and high-performance computing tasks. This is a hands-on leadership position that combines technical expertise with team management skills to deliver high-quality software products. Primary responsibilities : Software Development : - Develop and maintain high-performance applications using C++ and CUDA. - Design and implement parallel algorithms for GPUs to accelerate computational workloads. Performance Optimization : - Optimize CUDA kernels for performance, scalability, and memory efficiency. - Analyze performance bottlenecks and propose innovative solutions. Code Review and Testing : - Conduct code reviews to ensure adherence to coding standards and best practices. - Develop and execute test cases to validate functionality and performance. Collaboration : - Work closely with the software engineering and research teams to understand requirements and deliver robust solutions. - Provide technical guidance and mentoring to junior team members when necessary. Documentation : - Write and maintain technical documentation, including design specifications and user manuals. Required Skills : - C++ : Strong proficiency in modern C++ (C++11/14/17/20). - CUDA Programming : Extensive experience in developing, debugging, and optimizing CUDA applications. - GPU Optimization : Familiarity with memory hierarchy, shared memory, streams, and warp-level operations in CUDA. - Parallel Computing : Solid understanding of parallel algorithms and multi-threaded programming. - Mathematical and Analytical Skills : Strong foundation in linear algebra, calculus, and numerical methods. - Tools : Experience with debugging/profiling tools like Nsight, CUDA Memcheck, or similar.

Company: IT Services Organization Key Skills: Machine Learning, AI, Artificial Intelligence, Data Science, Gen AI, C/C++, CUDA, Docker, Kubernetes, Full Stack Development, Cloud (AWS/Azure/VM), Networking Tools Roles and Responsibilities: Lead the design and implementation of AI solutions across various platforms. Develop and optimize machine learning models and algorithms. Collaborate with cross-functional teams to integrate AI capabilities into existing systems. Oversee the transformation of data from CAD, Simulation, and JT files into Open VSD formats. Manage full-stack development, ensuring seamless interaction between front-end and back-end services. Utilize strong knowledge of C/C++ and parallel programming techniques, including CUDA C/C++. Demonstrate expertise in containerization and virtualization technologies, including Docker and Kubernetes. Diagnose and resolve networking issues using tools such as Wireshark and Iperf. Set up and manage workloads on various Cloud Service Providers (AWS, Azure, VM). Travel as required for conferences and on-site collaboration with developers. Experience Requirement: 15-20 years of experience in the IT industry with extensive exposure to Machine Learning and Artificial Intelligence projects. Proven experience in designing and delivering large-scale AI and ML systems and managing their life cycle. Hands-on experience with full-stack development, cloud deployment strategies, and performance optimization. Demonstrated capability in containerized environments using Docker and Kubernetes. Solid track record of troubleshooting network and infrastructure-level issues using tools such as Wireshark and Iperf. Prior experience collaborating with cross-functional global teams and integrating AI into enterprise systems. Education: Any Graduation.

This is a remote position. We are seeking a Senior Software Engineer: Numerical and High Performance Computing to join our team. Responsibilites: Work with modern numerical computing and deep learning technology. Contribute to open-source projects like PyTorch, TorchRL, TorchAO, and Triton. Use C++, CUDA, Python, C, and MLIR. Develop new features, performance enhancements and help maintain code with millions of users. Interact with the PyTorch development community, users, and our clients who are building PyTorch and building with PyTorch. Collaborate with, learn from, and mentor team members. Be a part of the Python community. Requirements Prior experience with particular packages like PyTorch or NumPy is nice to have but certainly not required. If you have expertise in languages like C++, Python, and CUDA and can find your way around large code bases, we expect you can learn about any new library or project quickly. We also highly value good communication skills. Knowledge of deep learning is of benefit. We aim to help you grow within company and within the Python open source community. Benefits Work Location: Remote 5 days working

Job Title : Computer Vision Engineer. Location : Gachibowli. Experience : 1-3 years. Employment Type : Full-time. About The Role We are looking for a passionate and skilled Computer Vision Engineer to join our cutting-edge simulation and defense technology team. Role This role is instrumental in building next-generation simulation environments that incorporate AI, computer vision, and immersive sensory realism for military training and war-gaming Strategic Objectives : Develop modular AI/ML-enhanced simulation blocks to support complex virtual training scenarios. Build real-time Computer Vision/Image Processing capabilities for enhanced situational awareness. Improve sensory realism including vision, sound, and motion to deliver truly immersive experiences. Enable adaptive learning and behavioral modeling for intelligent virtual entities. Contribute to the realization of a Military War Room / Gaming Command Centre with dynamic, data-driven Responsibilities : Design and implement AI/ML models tailored for simulation use-cases. Integrate image processing and computer vision techniques into real-time simulation pipelines. Collaborate with domain experts to translate training and tactical requirements into virtual environments. Optimize system performance for real-time execution and immersive responsiveness. Prototype and deploy modules involving visual perception, motion prediction, and behavioral : Strong mathematical foundation in Linear Algebra, Calculus, Probability, and Optimization. Proficiency in Python and/or C/C++ for simulation and ML development. Demonstrated understanding of AI/ML algorithms and computer vision : Experience with OpenCV, YOLO, SLAM, depth estimation techniques. Familiarity with NVIDIA NVAPI, CUDA programming, or real-time graphics APIs. Background in developing simulations or real-time systems in gaming, defense, or robotics We Offer : Opportunity to work on impactful, mission-critical defense simulation projects. A collaborative team of engineers, data scientists, and defense experts. Continuous learning and access to the latest technologies in AI, ML, and simulation. (ref:hirist.tech)

We're seeking a highly motivated individual to join our growing team in Visakhapatnam, India! You'll play a key role in developing and maintaining cutting-edge 3D applications, leveraging the latest advancements in rendering, modeling, and simulation technologies. Responsibilities : Design, develop, and Optimise 3D software applications using your Python expertise. Implement and refine algorithms to push the boundaries of 3D graphics, rendering, and simulation. Collaborate seamlessly with cross-functional teams (UI/UX designers, fellow engineers) to create an exceptional user experience. Troubleshoot and resolve software defects to ensure performance and stability. Stay at the forefront of the 3D graphics and software development landscape. Document software features and processes for clear communication and maintainability. Requirements : Minimum 2 Yrs Experience Bachelor's degree in Computer Science, Software Engineering, or a related field. Basic understanding of 3D software development with Python. Bonus Points: Experience with GPU programming (CUDA, OpenGL, Vulkan). Knowledge of game engines (Unity, Unreal Engine). Understanding of physics simulation and rendering techniques. Basic understanding of 3D graphics principles (rendering, shading, modeling). Experience with 3D graphics libraries (OpenGL, DirectX, Vulkan) is a plus. Familiarity with Agile development methodologies. What We Offer: Work on cutting-edge 3D technology projects and contribute to a growing company. Collaborative and supportive work environment where you can learn and thrive. Excited to join our team? If you're passionate about 3D technology and driven to make a difference, submit your resume/CV to careers@protomedialive.com.

Roles & Responsibilities Design, implement, and train deep learning models for: Text-to-Speech (e.g., SpeechT5, StyleTTS2, YourTTS, XTTS-v2 or similar models) Voice Cloning with speaker embeddings (x-vectors, d-vectors), few-shot adaptation, prosody and emotion transfer Engineer multilingual audio-text preprocessing pipelines: Text normalization, grapheme-to-phoneme (G2P) conversion, Unicode normalization (NFC/NFD) Silence trimming, VAD-based audio segmentation, audio enhancement for noisy corpora, speech prosody modification and waveform manipulation Build scalable data loaders using PyTorch for: Large-scale, multi-speaker datasets with variable-length sequences and chunked streaming Extract and process acoustic features: Log-mel spectrograms, pitch contours, MFCCs, energy, speaker embeddings Optimize training using: Mixed precision (FP16/BFloat16), gradient checkpointing, label smoothing, quantization-aware training Build serving infrastructure for inference using: TorchServe, ONNX Runtime, Triton Inference Server, FastAPI (for REST endpoints), including batch and real-time modes Optimize models for production: Quantization, model pruning, ONNX conversion, parallel decoding, GPU/CPU memory profiling Create automated and human evaluation logics: MOS, PESQ, STOI, BLEU, WER/CER, multi-speaker test sets, multilingual subjective listening tests Implement ethical deployment safeguards: Digital watermarking, impersonation detection, and voice verification for cloned speech Conduct literature reviews and reproduce state-of-the-art papers; adapt and improve on open benchmarks Mentor junior contributors, review code, and maintain shared research and model repositories Collaborate across teams (MLOps, backend, product, linguists) to translate research into deployable, user-facing solutions Required Skills Advanced proficiency in Python and PyTorch (TensorFlow a plus) Strong grasp of deep learning concepts: Sequence-to-sequence models, Transformers, autoregressive and non-autoregressive decoders, attention mechanisms, VAEs, GANs Experience with modern speech processing toolkits: ESPnet, NVIDIA NeMo, Coqui TTS, OpenSeq2Seq, or equivalent Design custom loss function for custom models based on: Mel loss, GAN loss, KL divergence, attention losses, etc.,, learning rate schedules, training stability Hands-on experience with multilingual and low-resource language modeling Understanding of transformer architecture, LLMs and working with existing AI models, tools and APIs Model serving & API integration: TorchServe, FastAPI, Docker, ONNX Runtime Preferred (Bonus) Skills CUDA kernel optimization, custom GPU operations, memory footprint profiling Experience deploying on AWS/GCP with GPU acceleration Experience developing RESTful APIs for real-time TTS/voice cloning endpoints Publications or open-source contributions in TTS, ASR, or speech processing Working knowledge of multilingual translation pipelines Knowledge of speaker diarization, voice anonymization, and speech synthesis for agglutinative/morphologically rich languages Milestones & Expectations (First 3–6 Months) Deliver at least one production-ready TTS or Voice Cloning model integrated with India Speaks’ Dubbing Studio or SaaS APIs Create a fully reproducible experiment pipeline for multilingual speech modeling, complete with model cards and performance benchmarks Contribute to custom evaluation tools for measuring quality across Indian languages Deploy optimized models to live staging environments using Triton, TorchServe, or ONNX Demonstrate impact through real-world integration in education, media, or defence deployments

Institute of Information Science Postdoctoral Researcher 2 Person The Computer Systems Laboratory - Machine Learning Systems Team Focuses On Research Areas Including Parallel And Distributed Computing, Compilers, And Computer Architecture. We Aim To Leverage Computer System Technologies To Accelerate The Inference And Training Of Deep Learning Models And Develop Optimizations For Next-generation AI Models. Our Research Emphasizes The Following Job Description Unit Institute of Information Science JobTitle Postdoctoral Researcher 2 Person Work Content Research on Optimization of Deep Learning Model Inference and Training AI Model Compression and Optimization Model Compression Techniques (e.g., Pruning And Quantization) Reduce The Size And Computational Demands Of AI Models, Which Are Crucial For Resource-constrained Platforms Such As Embedded Systems And Memory-limited AI Accelerators. We Aim To Explore AI compiler: deployment methods for compressed models across servers, edge devices, and heterogeneous systems. High performance computing: efficient execution of compressed models on processors with advanced AI extensions, e.g., Intel AVX512, ARM SVE, RISC-V RVV, and tensor-level accelerations on GPUs and NPUs. AI Accelerator Design We aim to design AI accelerators for accelerating AI model inference, focusing on software and hardware co-design and co-optimization. Optimization of AI Model Inference in Heterogeneous Environments Computer Architectures Are Evolving Toward Heterogeneous Multi-processor Designs (e.g., CPUs + GPUs + AI Accelerators). Integrating Heterogeneous Processors To Execute Complex Models (e.g., Hybrid Models, Multi-models, And Multi-task Models) With High Computational Efficiency Poses a Critical Challenge. We Aim To Explore Efficient scheduling algorithms. Parallel algorithms for the three dimensions: data parallelism, model parallelism, and tensor parallelism. Qualifications Ph.D. degree in Computer Science, Computer Engineering, or Electrical Engineering Experience in parallel computing and parallel programming (CUDA or OpenCL, C/C++ programming) or hardware design (Verilog or HLS) Proficient in system and software development Candidates With The Following Experience Will Be Given Priority Experience in deep learning platforms, including PyTorch, TensorFlow, TVM, etc. Experience in high-performance computing or embedded systems. Experience in algorithm designs. Knowledge of compilers or computer architecture Working Environment Operating Hours 8:30AM-5:30PM Work Place Institute of Information Science, Academia Sinica Treatment According to Academia Sinica standards: Postdoctoral Researchers: NT$64,711-99,317/month. Benefits include: labor and healthcare insurance, and year-end bonuses. Reference Site 洪鼎詠網頁: http://www.iis.sinica.edu.tw/pages/dyhong/index_zh.html, 吳真貞網頁: http://www.iis.sinica.edu.tw/pages/wuj/index_zh.html Please Email Your CV (including Publications, Projects, And Work Experience), Transcripts (undergraduate And Above), And Any Other Materials That May Assist In The Review Process To The Following PIs Acceptance Method Contacts Dr. Ding-Yong Hong Contact Address Room 818, New IIS Building, Academia Sinica Contact Telephone 02-27883799 ext. 1818 Email dyhong@iis.sinica.edu.tw Required Documents Dr. Ding-Yong Hong: dyhong@iis.sinica.edu.tw Dr. Jan-Jan Wu: wuj@iis.sinica.edu.tw Precautions for application Date Publication Date 2025-01-20 Expiration Date 2025-12-31

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

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

Sprinklr was founded in 2009 to solve a big problem: enterprise-size complexity dividing brands from the customers they serve. The idea was to unify silos, technology, and teams across large, complex companies. What started in social expanded into a single AI platform to reach, engage, and listen to customers on more than 30 digital channels. Today, Sprinklr has an AI-based platform for four product suites: Sprinklr Service, Sprinklr Social, Sprinklr Marketing and Sprinklr Insights. And as customer-facing teams, markets, and geographies work together, brands benefit from a unified digital edge. At Sprinklr, the culture is built around pushing the limits of possibility. Social media today has changed the world, with each of us playing a role in the future. The Company strives to delight customers by going above and beyond as a trusted social guide and helping reimagine enterprise software for the better, with a core belief in the Sprinklr Way. Job Title: Director of Machine Learning Research Location: Gurugram Responsibilities: Set Research Direction: Define and execute a forward-looking ML research agenda aligned with company strategy and business objectives and technological innovation. Drive Key Research in relevant areas of Sprinklr: Self-learning AI Agents, Auto Evaluation of AI Agents, Taxonomy discovery and quality, End-to-end voice models, Multi-linguality, Multi-modality, Domain-Specific finetuning and alignment, etc. Advance the State of the Art: Guide research initiatives in areas such as deep learning, generative models, NLP, and reinforcement learning. Encourage and support filing patents and publications in top-tier venues (NeurIPS, ICML, ACL, CVPR, IEEE etc.). Bridge Research and Product: Collaborate with engineering and product teams to transition research into real-world applications. Champion best practices in experimentation, reproducibility, and scalability. Lead and Grow the Team: Mentor and manage a high-performing team of ML researchers and engineers. Foster a culture of curiosity, rigor, and excellence. Act as a Thought Leader: Stay ahead of emerging trends and shape the company's position in the global AI landscape. Evangelism : Serve as a subject matter expert internally and externally. Represent the company in academic and industry events, talks, and panels. Qualifications: Deep expertise in modern ML techniques, especially large-scale learning, generative models, or foundational models. Experienced with synthetic dataset generation for production and quantization. PhD in Computer Science, Machine Learning, or a related field. 8+ years of experience in ML/AI, including 4+ years in a leadership role. Proven track record of impactful research contributions, including publications, patents, or open-source work. Strong knowledge of cloud platform technologies and MLops tools such as CUDA, K8s, Docker, PyTorch, TensorRT, etc. Strong leadership and communication skills, with experience managing senior researchers and cross-functional collaboration. Ability to align research investments with business strategy and measurable outcomes.