54 Kubeflow Jobs - Page 2

Position overview: We are seeking a ML Engineer with a strong background in Machine Learning, Natural Language Processing (NLP), Generative AI, and Retrieval-Augmented Generation (RAG). The ideal candidate will possess 1+ years of hands-on experience in developing and deploying advanced data-driven solutions. You will play a key role in our AI-CoE team, contributing to cutting-edge projects that drive innovation and business value. A special focus area for this role would be to build AI enabled products that would result in the creation of monetizable product differentiators for Tata Communications products and services. Detailed job description & Key Responsibilities: Design, develop, and deploy machine learning systems, including Generative AI models and LLMs. Research and implement state-of-the-art ML algorithms and tools. Conduct data preprocessing, feature engineering, and statistical analysis. Train, fine-tune, and optimize machine learning models for performance and accuracy. Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts. Extend existing ML frameworks and libraries to meet project requirements. Stay updated with the latest advancements in machine learning and AI. Skills: Working knowledge of machine learning and deep learning skills. Strong knowledge of programming knowledge Python, SQL and commonly used frameworks & tools PyTorch, Sci-kit, NumPy, Gen AI tools like langchain/llamaIndex Working knowledge of MLOPs principles and implementing projects with Big Data in batch and streaming mode. - must have Experience in handling databases (SQL and NoSQL) Exposure to MLFlow, KubeFlow, Git CI/CD Experience with containerization tools like Docker, and orchestration tools like Kubernetes Excellent problem-solving skills and a proactive attitude. Strong communication and teamwork abilities. Ability to manage multiple projects and meet deadlines Interview will involve coding test

Position overview: We are seeking a ML Engineer with a strong background in Machine Learning, Natural Language Processing (NLP), Generative AI, and Retrieval-Augmented Generation (RAG). The ideal candidate will possess 1+ years of hands-on experience in developing and deploying advanced data-driven solutions. You will play a key role in our AI-CoE team, contributing to cutting-edge projects that drive innovation and business value. A special focus area for this role would be to build AI enabled products that would result in the creation of monetizable product differentiators for Tata Communications products and services. Detailed job description & Key Responsibilities: Design, develop, and deploy machine learning systems, including Generative AI models and LLMs. Research and implement state-of-the-art ML algorithms and tools. Conduct data preprocessing, feature engineering, and statistical analysis. Train, fine-tune, and optimize machine learning models for performance and accuracy. Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts. Extend existing ML frameworks and libraries to meet project requirements. Stay updated with the latest advancements in machine learning and AI. Skills: Working knowledge of machine learning and deep learning skills. Strong knowledge of programming knowledge Python, SQL and commonly used frameworks & tools PyTorch, Sci-kit, NumPy, Gen AI tools like langchain/llamaIndex Working knowledge of MLOPs principles and implementing projects with Big Data in batch and streaming mode. - must have Experience in handling databases (SQL and NoSQL) Exposure to MLFlow, KubeFlow, Git CI/CD Experience with containerization tools like Docker, and orchestration tools like Kubernetes Excellent problem-solving skills and a proactive attitude. Strong communication and teamwork abilities. Ability to manage multiple projects and meet deadlines Interview will involve coding test

Position overview: We are seeking a Data Scientist with a strong background in Machine Learning, Natural Language Processing (NLP), Generative AI, and Retrieval-Augmented Generation (RAG). The ideal candidate will possess 1+ years of hands-on experience in developing and deploying advanced data-driven solutions. You will play a key role in our AI-CoE team, contributing to cutting-edge projects that drive innovation and business value. A special focus area for this role would be to build AI enabled products that would result in the creation of monetizable product differentiators. Detailed job description & Key Responsibilities: Develop, Test, and Deploy machine learning models for various business and Telco use cases. Perform data preprocessing, feature engineering and ML/DL model evaluation. Optimize and fine-tune models for performance and scalability. Good understanding of NLP concepts and projects involving entity recognition, text classification, and language modelling like GPT/Llama/Claude/Grok Build and refine RAG models to improve information retrieval and answer generation systems. Integrate RAG methods into existing applications to enhance data accessibility and user experience. Work closely with cross-functional teams including software engineers, product managers, and domain experts. Communicate technical concepts to non-technical stakeholders effectively. Document processes, methodologies, and model development for internal and external stakeholders. Skills: Strong knowledge of probability and statistics. Working knowledge of machine learning and deep learning skills. Strong knowledge of programming knowledge Python, SQL and commonly used frameworks & tools PyTorch, Sci-kit, NumPy, Gen AI tools like langchain/llamaIndex Working knowledge of MLOPs principles and implementing projects with Big Data in batch and streaming mode. Excellent problem-solving skills and a proactive attitude. Strong communication and teamwork abilities. Ability to manage multiple projects and meet deadlines Interview will involve coding tests.

Position overview: We are seeking a Data Scientist with a strong background in Machine Learning, Natural Language Processing (NLP), Generative AI, and Retrieval-Augmented Generation (RAG). The ideal candidate will possess 1+ years of hands-on experience in developing and deploying advanced data-driven solutions. You will play a key role in our AI-CoE team, contributing to cutting-edge projects that drive innovation and business value. A special focus area for this role would be to build AI enabled products that would result in the creation of monetizable product differentiators. Detailed job description & Key Responsibilities: Develop, Test, and Deploy machine learning models for various business and Telco use cases. Perform data preprocessing, feature engineering and ML/DL model evaluation. Optimize and fine-tune models for performance and scalability. Good understanding of NLP concepts and projects involving entity recognition, text classification, and language modelling like GPT/Llama/Claude/Grok Build and refine RAG models to improve information retrieval and answer generation systems. Integrate RAG methods into existing applications to enhance data accessibility and user experience. Work closely with cross-functional teams including software engineers, product managers, and domain experts. Communicate technical concepts to non-technical stakeholders effectively. Document processes, methodologies, and model development for internal and external stakeholders. Skills: Strong knowledge of probability and statistics. Working knowledge of machine learning and deep learning skills. Strong knowledge of programming knowledge Python, SQL and commonly used frameworks & tools PyTorch, Sci-kit, NumPy, Gen AI tools like langchain/llamaIndex Working knowledge of MLOPs principles and implementing projects with Big Data in batch and streaming mode. Excellent problem-solving skills and a proactive attitude. Strong communication and teamwork abilities. Ability to manage multiple projects and meet deadlines Interview will involve coding tests.

We are looking for experienced Senior and Principal Machine Learning Engineers across two teams: InstructLab and OpenShift AI . In this role, you will build, optimize, and scale machine learning models while contributing to innovative AI-driven solutions, and assisting users in understanding ML predictions. During the hiring process, we'll work with you to determine the best team placement based on your background and interests. While the core job requirements remain consistent, your day-to-day responsibilities will align with your chosen team's objectives. Successful applicants must reside in a country where Red Hat is registered to do business. What you will do Specific responsibilities will vary based on team placement, but may include: Design and implement machine learning systems Develop and optimize ML models for production use Create and maintain ML infrastructure and pipelines Ensure ML systems are scalable and maintainable Collaborate with data scientists to productionize models Collaborate closely with researchers, software developers, and upstream AI/ML communities Mentor and guide other team members What you will bring Experience in AI development, deep learning, machine learning libraries (e.g. pytorch, scikit-learn), prompt engineering, and/or fundamental mathematics Experience in feature engineering Experience in Go or Python development Experience in Kubernetes, OpenShift, Docker, or other cloud-native technologies Experience in agile development, Jira, and Git Ability to quickly learn and use new tools and technologies Excellent written and verbal communication skills The following skills are valued and may influence team placement: Masters or PhD in Machine Learning (ML) or Natural Language Processing (NLP) Active participation in KServe, TrustyAI, Kubeflow, or other open source communities Specialized expertise in specific AI domains (NLP, Computer Vision, MLOps, etc.)

HPE is seeking Data Engineer with strong experience in machine learning workflows to build and optimize scalable data systems. You'll work closely with data scientists and data engineers to power ML-driven solutions. Responsibilities: Collaborate closely with Machine Learning (ML) teams to deploy and monitor models in production, ensuring optimal performance and reliability. Design and implement experiments, and apply statistical analysis to validate model solutions and results. Lead efforts in ensuring high-quality data, proper governance practices, and excellent system performance in complex data architectures. Develop, maintain, and scale data pipelines, enabling machine learning and analytical models to function efficiently. Monitor and troubleshoot issues within data systems, resolving performance bottlenecks and implementing best practices. Required Skills: 56 years of data engineering experience, with a proven track record in building scalable data systems. Proficiency in SQL & NoSQL databases, Python, and distributed processing technologies such as Apache Spark. Strong understanding of data warehousing concepts, data modelling, and architecture principles. Expertise in cloud platforms (AWS, GCP, Azure) and managing cloud-based data systems would be an added advantage Hands-on experience building and maintaining machine learning pipelines and utilizing tools like MLflow, Kubeflow, or similar frameworks. Experience with search, recommendation engines, or NLP (Natural Language Processing) technologies. Solid foundation in statistics and experimental design, particularly in relation to machine learning systems. Strong problem-solving skills and ability to work independently and in a team-oriented environment.

Develop and deploy ML pipelines using MLOps tools, build FastAPI-based APIs, support LLMOps and real-time inferencing, collaborate with DS/DevOps teams, ensure performance and CI/CD compliance in AI infrastructure projects. Required Candidate profile Experienced Python developer with 4–8 years in MLOps, FastAPI, and AI/ML system deployment. Exposure to LLMOps, GenAI models, containerized environments, and strong collaboration across ML lifecycle

We are looking for Indias top 1% Machine Learning Engineers for a unique job opportunity to work with the industry leaders Who can be a part of the community? We are looking for top-tier Machine Learning Engineers with expertise in building, deploying, and optimizing AI models If you have experience in this field then this is your chance to collaborate with industry leaders Whats in it for you? Pay above market standards The role is going to be contract based with project timelines from 2-6 months, or freelancing Be a part of Elite Community of professionals who can solve complex AI challenges Responsibilities: Design, optimize, and deploy machine learning models; implement feature engineering and scaling pipelines Use deep learning frameworks (TensorFlow, PyTorch) and manage models in production (Docker, Kubernetes) Automate workflows, ensure model versioning, logging, and real-time monitoring; comply with security and regulations Work with large-scale data, develop feature stores, and implement CI/CD pipelines for model retraining and performance tracking Required Skills: Proficiency in machine learning, deep learning, and data engineering (Spark, Kafka) Expertise in MLOps, automation tools (Docker, Kubernetes, Kubeflow, MLflow, TFX), and cloud platforms (AWS, GCP, Azure) Strong knowledge of model deployment, monitoring, security, compliance, and responsible AI practices Nice to Have: Experience with A/B testing, Bayesian optimization, and hyperparameter tuning Familiarity with multi-cloud ML deployments and generative AI technologies (LLM fine-tuning, FAISS). Locations : Mumbai, Delhi / NCR, Bengaluru , Kolkata, Chennai, Hyderabad, Ahmedabad, Pune, India

Why Join? Above market-standard compensation Contract-based or freelance opportunities (212 months) Work with industry leaders solving real AI challenges Flexible work locations Remote | Onsite | Hyderabad/Bangalore Your Role: Architect and optimize ML infrastructure with Kubeflow, MLflow, SageMaker Pipelines Build CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI/CD) Automate ML workflows (feature engineering, retraining, deployment) Scale ML models with Docker, Kubernetes, Airflow Ensure model observability, security, and cost optimization in cloud (AWS/GCP/Azure) Must-Have Skills: Proficiency in Python, TensorFlow, PyTorch, CI/CD pipelines Hands-on experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML) Expertise in monitoring tools (MLflow, Prometheus, Grafana) Knowledge of distributed data processing (Spark, Kafka) (Bonus: Experience in A/B testing, canary deployments, serverless ML)

Roles and Responsibilities Design, Develop, and Deploy : Develop, deploy, and maintain machine learning models that are not only theoretically sound but also practical and scalable. Our team places a strong emphasis on rapid, trustworthy experimentation for validating models and features. Model Maintenance : Design and build machine learning pipelines optimized for scalability, ensuring seamless model training, evaluation, and deployment. Monitor the performance of machine learning models in real-time using statistical methods, ensuring their efficiency and effectiveness.Implement and manage real-time data systems to handle large data streams efficiently. Technical Expertise : Conduct RD in innovative techniques such as Recommender Systems, Computer Vision, NLP, Generative AI and Causal Inference, pushing the boundaries of practical machine learning applications. Software Development : Develop robust, scalable, and maintainable software solutions for seamless model deployment. CI/CD Pipelines : Set up and manage Continuous Integration/Continuous Deployment (CI/CD) pipelines for automated testing, deployment, and model integration. Collaboration : Work closely with the Product Managers, Platforms and Engineering teams to ensure smooth deployment and integration of ML models into Myntra production systems. Data Management : Utilize big data technologies and data lakes to preprocess and shape raw data for machine learning applications. Code Quality : Write clean, efficient, and maintainable code following best practices. Performance Optimization : Conduct performance testing, troubleshooting, and tuning to ensure optimal model performance. Continuous Learning : Stay up-to-date with the latest advancements in machine learning and technology, sharing insights and knowledge across the organization. Experience Industry Experience: Master's degree in a related technical field with 4+ years of relevant industry experience or Bachelor's degree in Computer Science, Data Science, Machine Learning, Statistics, or a related technical field with 6+ years of relevant industry experience OR OR Ph.D. in a related field with a thesis in a domain relevant to Myntra's needs (e.g., Recommender Systems, Natural Language Processing). Machine Learning Expertise: At least 4 years of hands-on experience as a Machine Learning Engineer or a similar role. Solid understanding of statistics, particularly as it applies to machine learning, including probability theory, hypothesis testing, and statistical inference. Production Deployment: Proven track record of implementing and scaling machine learning models and pipelines in a production environment. Programming Skills: Strong proficiency in Python or equivalent programming languages for model development. ML Frameworks: Familiarity with leading machine learning frameworks (Keras, TensorFlow, PyTorch) and libraries (scikit-learn). CI/CD Tools: Experience with CI/CD tools and practices. Communication: Excellent verbal and written communication skills. Teamwork Independence: Ability to work collaboratively in a team environment or independently as needed. Mentor team members technically on designing and deploying ML pipelines and services. Workload Management: Strong organizational skills to manage and prioritize tasks, supporting your manager effectively. Preferred Qualifications Strong emphasis on rapid, trustworthy experimentation for validating machine learning models and hypotheses. Hands-on experience with Search and Recommender Systems, Computer Vision, or Forecasting is strongly desired. We value candidates who emphasize practical implementation and scaling of machine learning solutions. Experience with real-time systems and databases like Kafka, Cassandra, Vector Databases, or Bigtable is highly valued. Prior experience with Generative AI techniques earns brownie points. Advanced understanding and experience in Causal Inference earns a lot of brownie points. Strong communication skills, especially in conveying complex technical and statistical concepts to a non-technical audience. Experience with big data technologies like Spark or other distributed computing frameworks. Exceptional candidates are encouraged to apply, even if you don't meet every listed qualification. We're open to hiring individuals who demonstrate outstanding potential. Nice to Have Research Contributions: Publications or presentations in recognized Machine Learning and Data Science journals/conferences. Cloud Services: Proficiency in cloud platforms (AWS, Google Cloud) and an understanding of distributed systems. Generative AI Exposure: Familiarity with Generative AI models. Database Management: Experience with SQL and/or NoSQL databases. ML Orchestration: Knowledge of ML orchestration tools (Airflow, Kubeflow, MLFlow).

In this role, you will drive the end-to-end development and deployment of AI/ML solutions on Cloud, applying your expertise in machine learning, MLOps, and GenAI. You will mentor junior engineers by establishing best practices and guiding solution design. Additionally, you will design, implement, and optimize advanced machine learning algorithms using robust data science techniques, while driving the scaling and adoption of AI/ML technologies to meet strategic business objectives. If you have a passion for AI, cloud-based ML solutions, and driving scalable innovation, this role is for you. You have: Bachelors or masters degree in computer science, Engineering, or related field with 8+ years of experience in machine learning, data science, and statistics. Expertise in ML algorithms with strong Python and SQL skills, including Jupyter notebooks. Proven knowledge of GenAI and Agentic AI technologies. Hands-on experience with MLOps tools like Kubeflow, Vertex AI, and GenAI/LLMops. Grip on, Kubernetes, Docker, and microservices architecture. It would be nice if you also had: Experience with OpenAI, Llama, and Lang Chain frameworks. Practical experience in Pytest and Rust programming. Prior experience developing AI applications in the telecom domain. Google Cloud Professional Machine Learning Engineer certification. Architect and develop AI/ML use cases on Cloud, driving projects from discovery and proof of concept through industrialization, deployment, and ongoing maintenance. Mentor and guide junior engineers in defining robust methodologies and evaluating the most effective technical solutions. Propose, implement, test, and select optimal machine learning algorithms by leveraging advanced statistical tools and data science techniques to uncover insights and generate accurate predictions. Drive the rapid scaling and integration of AI and ML technologies to enhance system performance and support strategic business objectives.

Roles and Responsibilities Design, Develop, and Deploy: Create and implement advanced machine learning models and algorithms to tackle complex business challenges across various domains, including Recommender Systems, Search, Computer Vision, Supply Chain Management (SCM), Pricing, Forecasting, Trend and Virality Prediction, Generative AI, and more. Technical Expertise: Demonstrate deep theoretical knowledge and practical expertise in one or more areas, such as Natural Language Processing (NLP), Computer Vision, Recommender Systems, Gen AI and Optimization. Software Development: Develop robust, scalable, and maintainable software solutions for seamless model deployment. CI/CD Pipelines: Set up and manage Continuous Integration/Continuous Deployment (CI/CD) pipelines for automated testing, deployment, and model integration. Model Maintenance: Maintain and optimize machine learning pipelines, including data cleaning, feature extraction, and model training. Collaboration: Work closely with the Platforms and Engineering teams to ensure smooth deployment and integration of ML models into production systems. Data Management: Partner with the Data Platforms team to gather, process, and analyze data crucial for model development. Code Quality: Write clean, efficient, and maintainable code following best practices. Performance Optimization: Conduct performance testing, troubleshooting, and tuning to ensure optimal model performance. Continuous Learning: Stay up-to-date with the latest advancements in machine learning and technology, sharing insights and knowledge across the organization. Qualifications & Experience Industry Experience: 3-5+ years with a Bachelor's degree, or 2+ years with a Masters/Ph.D. in Computer Science, Mathematics, Statistics, or a related field. Machine Learning Expertise: At least 2 years of hands-on experience as a Machine Learning Engineer or a similar role. Production Deployment: Proven experience deploying machine learning solutions into production environments. Programming Skills: Strong proficiency in Python or equivalent programming languages for model development. ML Frameworks: Familiarity with leading machine learning frameworks (Keras, TensorFlow, PyTorch) and libraries (scikit-learn). CI/CD Tools: Experience with CI/CD tools and practices. Communication: Excellent verbal and written communication skills. Teamwork & Independence: Ability to work collaboratively in a team environment or independently as needed. Workload Management: Strong organizational skills to manage and prioritize tasks, supporting your manager effectively. Exceptional candidates are encouraged to apply, even if you don't meet every listed qualification. We're open to hiring individuals who demonstrate outstanding potential. Nice to Have Research Contributions: Publications or presentations in recognized Machine Learning and Data Science journals/conferences. Big Data Technologies: Experience with big data technologies like Spark or other distributed computing frameworks. Cloud Services: Proficiency in cloud platforms (AWS, Google Cloud) and an understanding of distributed systems. Generative AI Exposure: Familiarity with Generative AI models. Database Management: Experience with SQL and/or NoSQL databases. ML Orchestration: Knowledge of ML orchestration tools (Airflow, Kubeflow, MLFlow).

Job Overview As a Machine Learning Engineer, you will oversee the entire lifecycle of machine learning models. Your role involves collaborating with cross-functional teams, including data scientists, data engineers, software engineers, and DevOps specialists, to bridge the gap between experimental model development and reliable production systems. You will be responsible for automating ML pipelines, optimizing model training and serving, ensuring model governance, and maintaining the stability of deployed systems. This position requires a blend of experience in software engineering, data engineering, and machine learning systems, along with a strong understanding of DevOps practices to enable faster experimentation, consistent performance, and scalable ML operations. What You Will Do Work with Data Science Leadership and Stakeholders to understand business objectives, map scope of work, and support colleagues in achieving technical deliverables. Invest in strong relationships with colleagues and build a successful followership around a common goal. Build and optimize ML pipelines for feature engineering, model training, and inference. Develop low-latency, high-throughput model endpoints for distributed environments. Maintain cloud infrastructure for ML workloads, including GPUs/TPUs, across platforms like GCP, AWS, or Azure Troubleshoot, debug, and validate ML systems for performance and reliability. Write and maintain automated tests (unit and integration). Supports discussions with Data Engineers to work on data collection, storage, and retrieval processes. Collaborate with Data Governance to identify data issues and propose data cleansing or enhancement solutions. Drive continuous improvement efforts in enhancing performance and providing increased functionality, including developing processes for automation. Skills You Will Need Group Work Lead: Ability to lead portions of pod iteratives; can clearly communicate priorities and play an effective technical support role for colleagues. Communication: Maintaining timely communication with management and stakeholders on project progress, issues, and concerns. Developing effective communication plans tailored to diverse audiences. Consultive Mindset: Go beyond just providing analytics and actively engage stakeholders to understand their challenges and goals. Ability to have a business-first viewpoint when developing solutions. Cloud & ML Ops: Expertise in managing cloud-based ML infrastructures (GCP, AWS, or Azure), coupled with DevOps practices, ensures seamless model deployment, scalability, and system reliability. This includes containerization, CI/CD pipelines, and infrastructure-as-code tools. Proficiency in programming languages such as Python, SQL, and Java. Who You Are 5+ years of industry experience working with machine learning tools and technologies. Familiarity with agile development frameworks and collaboration tools (e.g., JIRA, Confluence). Experience using Tensorflow, PyTorch, scikit-learn, Kubeflow, pandas and numpy. and frameworks like Ray, Dask preferred. Expertise in data engineering, object-oriented programming, and familiarity with microservices and cloud technologies. An ongoing learner who seeks out emerging technology and can influence others to think innovatively. Gets energized by fast-paced environments and capable of supporting multiple projects - can identify primary and secondary objectives, prioritize time, and communicate timelines to team members. Dedicated to fulfilling ideals of diversity, inclusion, and respect that company aspires to achieve every day in every way. Regularly required to sit, talk, hear; use hands/fingers to touch, handle, and feel. Occasionally required to move about the workplace and reach with hands and arms. Requires close vision. Able to work a flexible schedule based on department and company needs.

Position: Senior AI Architect AI Factory (MLOps, GenOps)Experience:20+ years of total IT experience with a minimum of 10 years in AI/ML Proven experience in building scalable AI platforms or "AI Factories" for productionizing machine learning and generative AI workflows, including strong hands-on expertise in MLOps and emerging GenOps practices Location:Bangalore / Pune on case-to-case basisRole Summary:We are looking for a Senior AI Architect to lead the design and implementation of a next-generation AI Factory platform that streamlines the development, deployment, monitoring, and reuse of AI/ML and GenAI assets This role will be instrumental in establishing scalable MLOps and GenOps practices, building reusable components, standardizing pipelines, and enabling cross-industry solutioning for pre-sales and delivery The candidate will work closely with the AI Practice Head, contributing to both business enablement and technical strategy while supporting customer engagements, RFP/RFI responses, PoCs, and accelerator development Key Responsibilities: Architect and build the AI Factory a central repository of reusable AI/ML models, GenAI prompts, agents, pipelines, APIs, and accelerators Define and implement MLOps workflows for versioning, model training, deployment, CI/CD, monitoring, and governance Design and integrate GenOps pipelines for prompt engineering, LLM orchestration, evaluation, and optimization Create blueprints and templates for standardized AI solution delivery across cloud platforms (Azure, AWS, GCP) Build accelerators and reusable modules to speed up AI solutioning for common use cases (e g , chatbots, summarization, document intelligence) Enable pre-sales and solution teams with reusable assets for demos, PoCs, and customer presentations Contribute to RFP/RFI responses with scalable, production-ready AI factory strategies and architectural documentation Collaborate with data engineering, DevOps, cloud, and security teams to ensure robust and enterprise-compliant AI solution delivery Required Skills: Deep experience in MLOps tools like MLflow, Kubeflow, SageMaker Pipelines, Azure ML Pipelines, or Vertex AI Pipelines Understanding of GenOps frameworks including prompt flow management, LLM evaluation (e g , TruLens, Ragas), and orchestration (LangChain, LlamaIndex, Semantic Kernel) Strong command of Python, YAML/JSON, and API integration for scalable AI component development Experience with CI/CD pipelines (GitHub Actions, Jenkins, Azure DevOps), containerization (Docker, Kubernetes), and model registries Familiar with model observability, drift detection, automated retraining, and model versioning Ability to create clean, reusable architecture artifacts and professional PowerPoint decks for customer and internal presentations Preferred Qualifications: Experience in building and managing an enterprise-wide AI marketplace or model catalog Familiarity with LLMOps platforms (eg, Weights & Biases, PromptLayer, Arize AI) Exposure to multi-cloud GenAI architectures and hybrid deployment models Cloud certifications in AI/ML from any major provider (AWS, Azure, GCP) Soft Skills: Strong leadership and mentoring capabilities Effective communication and storytelling skills for technical and non-technical audiences Innovation mindset with a passion for automation and efficiency Comfortable working in a fast-paced, cross-functional environment with shifting priorities

Walk-in interview only -BLR No virtual Python & libraries scikit-learn, TensorFlow,PyTorch/XGBoost exp in supervised, unsupervised, and deep learning techniques Deployment tools Flask, FastAPI, Docker AWS GCP, Azure &ML,SQL Contact Maya 9880516218

Position Title: AI/ML Engineer Company: Cyfuture India Pvt. Ltd. Industry: IT Services and IT Consulting Location: Sector 81, NSEZ, Noida (5 Days Work From Office) Website: www.cyfuture.com About Cyfuture Cyfuture is a trusted name in IT services and cloud infrastructure, offering state-of-the-art data center solutions and managed services across platforms like AWS, Azure, and VMWare. We are expanding rapidly in system integration and managed services, building strong alliances with global OEMs like VMWare, AWS, Azure, HP, Dell, Lenovo, and Palo Alto. Position Overview We are hiring an experienced AI/ML Engineer to lead and shape our AI/ML initiatives. The ideal candidate will have hands-on experience in machine learning and artificial intelligence, with strong leadership capabilities and a passion for delivering production-ready solutions. This role involves end-to-end ownership of AI/ML projects, from strategy development to deployment and optimization of large-scale systems. Key Responsibilities Lead and mentor a high-performing AI/ML team. Design and execute AI/ML strategies aligned with business goals. Collaborate with product and engineering teams to identify impactful AI opportunities. Build, train, fine-tune, and deploy ML models in production environments. Manage operations of LLMs and other AI models using modern cloud and MLOps tools. Implement scalable and automated ML pipelines (e.g., with Kubeflow or MLRun). Handle containerization and orchestration using Docker and Kubernetes. Optimize GPU/TPU resources for training and inference tasks. Develop efficient RAG pipelines with low latency and high retrieval accuracy. Automate CI/CD workflows for continuous integration and delivery of ML systems. Key Skills & Expertise 1. Cloud Computing & Deployment Proficiency in AWS, Google Cloud, or Azure for scalable model deployment. Familiarity with cloud-native services like AWS SageMaker, Google Vertex AI, or Azure ML. Expertise in Docker and Kubernetes for containerized deployments Experience with Infrastructure as Code (IaC) using tools like Terraform or CloudFormation. 2. Machine Learning & Deep Learning Strong command of frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost. Experience with MLOps tools for integration, monitoring, and automation. Expertise in pre-trained models, transfer learning, and designing custom architectures. 3. Programming & Software Engineering Strong skills in Python (NumPy, Pandas, Matplotlib, SciPy) for ML development. Backend/API development with FastAPI , Flask , or Django . Database handling with SQL and NoSQL (PostgreSQL, MongoDB, BigQuery). Familiarity with CI/CD pipelines (GitHub Actions, Jenkins). 4. Scalable AI Systems Proven ability to build AI-driven applications at scale. Handle large datasets, high-throughput requests, and real-time inference. Knowledge of distributed computing: Apache Spark, Dask, Ray . 5. Model Monitoring & Optimization Hands-on with model compression, quantization, and pruning . A/B testing and performance tracking in production. Knowledge of model retraining pipelines for continuous learning. 6. Resource Optimization Efficient use of compute resources: GPUs, TPUs, CPUs . Experience with serverless architectures to reduce cost. Auto-scaling and load balancing for high-traffic systems. 7. Problem-Solving & Collaboration Translate complex ML models into user-friendly applications. Work effectively with data scientists, engineers, and product teams. Write clear technical documentation and architecture reports . Udisha Parashar Senior Talent Acquisition Specialist Mob: +91- 9301895707 Email: udisha.parashar@cyfuture.com URL: www.cyfuture.com

AI/ML Engineer Work on cutting-edge technologies like Natural Language Processing (NLP), Generative AI, and Large Language Models (LLMs) Assist in building intelligent systems for real-world applications Collaborate with data scientists and product teams on end-to-end ML workflows Cloud DevOps Engineer Get hands-on with AWS, Azure, or GCP cloud platforms Automate infrastructure, CI/CD pipelines, monitoring, and deployments Learn Infrastructure as Code (IaC) using tools like Terraform or Cloud Formation Software Engineer (Backend/Frontend) Build scalable applications using Python, Node.js for backend and React.js for frontend Contribute to the full-stack development lifecycle Participate in design, coding, testing, and debugging of software components MLOps Engineer Automate and scale ML workflows from development to production Work with tools like MLflow, Kubeflow, Docker, Kubernetes Ensure reproducibility, monitoring, and performance of ML models in production Data Engineer Develop robust ETL pipelines, data ingestion, and transformation logic Work with databases, data warehouses, and data lakes Handle real-time and batch data workflows using tools like Apache Airflow, Spark, Kafka Eligibility : BE/B.Tech/MCA/MSc 2023 or 2024 pass outs

Senior DevOps Engineer Experience: 5 - 9 Years Exp Salary : Competitive Preferred Notice Period : Within 30 Days Shift : 10:00AM to 7:00PM IST Opportunity Type: Onsite (Ahmedabad) Placement Type: Permanent (*Note: This is a requirement for one of Uplers' Clients) Must have skills required : Azure (Microsoft Azure), Docker/Terraform, TensorFlow, Python, AWS Good to have skills : Kubeflow, MLFlow Attri (One of Uplers' Clients) is Looking for: Senior DevOps Engineer who is passionate about their work, eager to learn and grow, and who is committed to delivering exceptional results. If you are a team player, with a positive attitude and a desire to make a difference, then we want to hear from you. Role Overview Description About Attri Attri is an AI organization that helps businesses initiate and accelerate their AI efforts. We offer the industrys first end-to-end enterprise machine learning platform, empowering teams to focus on ML development rather than infrastructure. From ideation to execution, our global team of AI experts supports organizations in building scalable, state-of-the-art ML solutions. Our mission is to redefine businesses by harnessing cutting-edge technology and a unique, value-driven approach. With team members across continents, we celebrate diversity, curiosity, and innovation. Were now looking for a Senior DevOps Engineer to join our fast-growing, remote-first team. If you're passionate about automation, scalable cloud systems, and supporting high-impact AI workloads, wed love to connect. What You'll Do (Responsibilities): Design, implement, and manage scalable, secure, and high-performance cloud-native infrastructure across Azure. Build and maintain Infrastructure as Code (IaC) using Terraform or CloudFormation. Develop event-driven and serverless architectures using AWS Lambda, SQS, and SAM. Architect and manage containerized applications using Docker, Kubernetes, ECR, ECS, or AKS. Establish and optimize CI/CD pipelines using GitHub Actions, Jenkins, AWS CodeBuild & CodePipeline. Set up and manage monitoring, logging, and alerting using Prometheus + Grafana, Datadog, and centralized logging systems. Collaborate with ML Engineers and Data Engineers to support MLOps pipelines (Airflow, ML Pipelines) and Bedrock with Tensorflow or PyTorch. Implement and optimize ETL/data streaming pipelines using Kafka, EventBridge, and Event Hubs. Automate operations and system tasks using Python and Bash, along with Cloud CLIs and SDKs. Secure infrastructure using IAM/RBAC and follow best practices in secrets management and access control. Manage DNS and networking configurations using Cloudflare, VPC, and PrivateLink. Lead architecture implementation for scalable and secure systems, aligning with business and AI solution needs. Conduct cost optimization through budgeting, alerts, tagging, right-sizing resources, and leveraging spot instances. Contribute to backend development in Python (Web Frameworks), REST/Socket and gRPC design, and testing (unit/integration). Participate in incident response, performance tuning, and continuous system improvement. Good to Have: Hands-on experience with ML lifecycle tools like MLflow and Kubeflow Previous involvement in production-grade AI/ML projects or data-intensive systems Startup or high-growth tech company experience Qualifications: Bachelors degree in Computer Science, Information Technology, or a related field. 5+ years of hands-on experience in a DevOps, SRE, or Cloud Infrastructure role. Proven expertise in multi-cloud environments (AWS, Azure, GCP) and modern DevOps tooling. Strong communication and collaboration skills to work across engineering, data science, and product teams. Benefits: Competitive Salary Support for continual learning (free books and online courses) Leveling Up Opportunities Diverse team environment How to apply for this opportunity: Easy 3-Step Process: 1. Click On Apply! And Register or log in on our portal 2. Upload updated Resume & Complete the Screening Form 3. Increase your chances to get shortlisted & meet the client for the Interview! About Our Client: Attri, an AI organization, leads the way in enterprise AI, offering advanced solutions and services driven by AI agents and powered by Foundation Models. Our comprehensive suite of AI-enabled tools drives business impact, enhances quality, mitigates risk, and also helps unlock growth opportunities. About Uplers: Our goal is to make hiring and getting hired reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant product and engineering job opportunities and progress in their career. (Note: There are many more opportunities apart from this on the portal.) So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!

Ready to build the future with AI At Genpact, we don&rsquot just keep up with technology&mdashwe set the pace. AI and digital innovation are redefining industries, and we&rsquore leading the charge. Genpact&rsquos AI Gigafactory, our industry-first accelerator, is an example of how we&rsquore scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. From large-scale models to agentic AI, our breakthrough solutions tackle companies most complex challenges. If you thrive in a fast-moving, innovation-driven environment, love building and deploying cutting-edge AI solutions, and want to push the boundaries of what&rsquos possible, this is your moment. Genpact (NYSE: G) is an advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions - we help companies across industries get ahead and stay ahead. Powered by curiosity, courage, and innovation, our teams implement data, technology, and AI to create tomorrow, today. Get to know us at genpact.com and on LinkedIn, X, YouTube, and Facebook. We invite applications for the role of Principal Consultant - Data Science Architect Job Description: We are seeking a highly experienced and visionary Data Science Architect to lead the design and implementation of scalable data science solutions across our organization. This role requires a strategic thinker with deep technical expertise in machine learning, data engineering, and analytics architecture. You will collaborate with cross-functional teams to drive innovation, optimize data workflows, and ensure the successful deployment of advanced analytics and AI solutions. Key Responsibilities: . Architect and implement end-to-end data science solutions, from data ingestion to model deployment and monitoring. . Collaborate with engineering, product, and business teams to define AI/ML strategies aligned with business goals. . Lead the selection and integration of data science tools, platforms, and frameworks. . Collaborate with data engineers, analysts, and business stakeholders to translate business problems into analytical solutions. . Define best practices for model governance, versioning, reproducibility, and scalability. . Mentor and guide data scientists and engineers on technical and architectural decisions. . Ensure data quality, security, and compliance across all data science initiatives. . Evaluate emerging technologies and trends in AI/ML to keep the organization at the forefront of innovation. Qualifications we seek in you! Minimum Qualifications . Bachelor%27s or Master&rsquos degree in Computer Science, Data Science, Statistics, or a related field (Ph.D. preferred). . experience in data science, with at least 2 years in an architect or lead role . Proven experience designing and deploying machine learning models in production environments. . Strong programming skills in Python, R, or Scala and experience with ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn). . Expertise with with GenAI, LLMs, or advanced NLP techniques. Experience in building GenAI or LLM-based applications. . Familiarity with big data technologies (e.g., Spark, Kafka, Delta Lake) and modern data stack components . Experience in product engineering . Certifications in cloud architecture or data science. . Good understanding of cloud platforms (AWS, Azure, GCP) and MLOps tools (MLflow, Kubeflow, etc.). . Excellent communication skills and the ability to influence technical and non-technical stakeholders. Why join Genpact . Lead AI-first transformation - Build and scale AI solutions that redefine industries . Make an impact - Drive change for global enterprises and solve business challenges that matter . Accelerate your career&mdashGain hands-on experience, world-class training, mentorship, and AI certifications to advance your skills . Grow with the best - Learn from top engineers, data scientists, and AI experts in a dynamic, fast-moving workplace . Committed to ethical AI - Work in an environment where governance, transparency, and security are at the core of everything we build . Thrive in a values-driven culture - Our courage, curiosity, and incisiveness - built on a foundation of integrity and inclusion - allow your ideas to fuel progress Come join the 140,000+ coders, tech shapers, and growth makers at Genpact and take your career in the only direction that matters: Up. Let&rsquos build tomorrow together. Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color, religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. Genpact is committed to creating a dynamic work environment that values respect and integrity, customer focus, and innovation. Furthermore, please do note that Genpact does not charge fees to process job applications and applicants are not required to pay to participate in our hiring process in any other way. Examples of such scams include purchasing a %27starter kit,%27 paying to apply, or purchasing equipment or training.

Senior Technical Architect Machine Learning Solutions We are looking for a Senior Technical Architect with deep expertise in Machine Learning (ML), Artificial Intelligence (AI) , and scalable ML system design . This role will focus on leading the end-to-end architecture of advanced ML-driven platforms, delivering impactful, production-grade AI solutions across the enterprise. Key Responsibilities Lead the architecture and design of enterprise-grade ML platforms , including data pipelines, model training pipelines, model inference services, and monitoring frameworks. Architect and optimize ML lifecycle management systems (MLOps) to support scalable, reproducible, and secure deployment of ML models in production. Design and implement retrieval-augmented generation (RAG) systems, vector databases , semantic search , and LLM orchestration frameworks (e.g., LangChain, Autogen). Define and enforce best practices in model development, versioning, CI/CD pipelines , model drift detection, retraining, and rollback mechanisms. Build robust pipelines for data ingestion, preprocessing, feature engineering , and model training at scale , using batch and real-time streaming architectures. Architect multi-modal ML solutions involving NLP, computer vision, time-series, or structured data use cases. Collaborate with data scientists, ML engineers, DevOps, and product teams to convert research prototypes into scalable production services . Implement observability for ML models including custom metrics, performance monitoring, and explainability (XAI) tooling. Evaluate and integrate third-party LLMs (e.g., OpenAI, Claude, Cohere) or open-source models (e.g., LLaMA, Mistral) as part of intelligent application design. Create architectural blueprints and reference implementations for LLM APIs, model hosting, fine-tuning, and embedding pipelines . Guide the selection of compute frameworks (GPUs, TPUs), model serving frameworks (e.g., TorchServe, Triton, BentoML) , and scalable inference strategies (batch, real-time, streaming). Drive AI governance and responsible AI practices including auditability, compliance, bias mitigation, and data protection. Stay up to date on the latest developments in ML frameworks, foundation models, model compression, distillation, and efficient inference . 14. Ability to coach and lead technical teams , fostering growth, knowledge sharing, and technical excellence in AI/ML domains. Experience managing the technical roadmap for AI-powered products , documentations ensuring timely delivery, performance optimization, and stakeholder alignment. Required Qualifications Bachelors or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 8+ years of experience in software architecture , with 5+ years focused specifically on machine learning systems and 2 years in leading team. Proven expertise in designing and deploying ML systems at scale , across cloud and hybrid environments. Strong hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face, Scikit-learn). Experience with vector databases (e.g., FAISS, Pinecone, Weaviate, Qdrant) and embedding models (e.g., SBERT, OpenAI, Cohere). Demonstrated proficiency in MLOps tools and platforms : MLflow, Kubeflow, SageMaker, Vertex AI, DataBricks, Airflow, etc. In-depth knowledge of cloud AI/ML services on AWS, Azure, or GCP – including certification(s) in one or more platforms. Experience with containerization and orchestration (Docker, Kubernetes) for model packaging and deployment. Ability to design LLM-based systems , including hybrid models (open-source + proprietary), fine-tuning strategies, and prompt engineering. Solid understanding of security, compliance , and AI risk management in ML deployments. Preferred Skills Experience with AutoML , hyperparameter tuning, model selection, and experiment tracking. Knowledge of LLM tuning techniques : LoRA, PEFT, quantization, distillation, and RLHF. Knowledge of privacy-preserving ML techniques , federated learning, and homomorphic encryption Familiarity with zero-shot, few-shot learning , and retrieval-enhanced inference pipelines. Contributions to open-source ML tools or libraries. Experience deploying AI copilots, agents, or assistants using orchestration frameworks.

Lead the development of scalable and efficient machine learning platform products. Develop technical specifications and architectures for new ML platform features and products. Collaborate with cross-functional teams across the globe to ensure product development aligns with business goals. Define and enforce engineering best practices to ensure high-quality deliverables. Participate in the code review process to ensure our code quality standards are met. Stay up-to-date with the latest ML platform technologies and trends to identify opportunities for product innovation. Participate in the hiring and onboarding process for new team members Communicate effectively with technical and non-technical stakeholders to ensure alignment on project goals and timelines. Required Skills and Experience - Candidate should have a Bachelors or Masters in Computer Science or equivalent Demonstrate Technical leadership with hands-on coding experience. 9+ years of experience of commercial software development with at least 3+ years working within the Machine Learning / AI space, Demonstrated excellence participating on cross functional teams in fast-paced environments, both in terms of technical leadership and hands-on coding. Excellent ability to break down complex problems into simple solutions. Willingness and ability to learn, evaluate, and make recommendations for leveraging new technologies. Strong analytical skills and desire to write clean, correct and efficient code. Sense of ownership, urgency and pride in your work. Proven that you are a leader who prioritizes, communicates clearly, and partners effectively with both technical and nontechnical employees. Excellent command of tools and expertise for troubleshooting production issues. Experience working with Python, Docker, Kubernetes. Experience with Kubeflow, Seldon, Spark and AWS / Azure cloud service experience is a plus.

Youll make a difference by: Siemens is seeking a visionary and technically strong Lead AI/ML Engineer to spearhead the development of intelligent systems that power the future of sustainable and connected transportation. This role will lead the design and deployment of AI/ML solutions across domains such as efficiency improvements in software development process, predictive maintenance, traffic analytics, computer vision for rail safety, and intelligent automation in rolling stock and rail infrastructure. Key Responsibilities Lead the end-to-end lifecycle of AI/ML projectsfrom data acquisition and model development to deployment and monitoringwithin the context of mobility systems. Architect scalable ML pipelines that integrate with Siemens Mobility platforms and other edge/cloud-based systems. Collaborate with multi-functional teams including domain experts, software architects, and system engineers to translate mobility use cases into AI-driven solutions. Mentor junior engineers and data scientists, and foster a culture of innovation, quality, and continuous improvement. Evaluate and integrate innovative research in AI/ML, including generative AI, computer vision, and time-series forecasting, into real-world applications. Ensure compliance with Siemens AI ethics, cybersecurity, and data governance standards. Required Qualifications Bachelor's or Masters or PhD in Computer Science, Machine Learning, Data Science, or a related field. 7+ years of experience in AI/ML engineering, with at least 2 years in a technical leadership role. Strong programming skills in Python and experience with ML frameworks such as TensorFlow, PyTorch, and Scikit-learn. Proven experience deploying ML models in production, preferably in industrial or mobility environments. Familiarity with MLOps tools (e.g., MLflow, Kubeflow) and cloud platforms (Azure, AWS, or GCP). Solid understanding of data engineering, model versioning, and CI/CD for ML. Preferred Qualifications Experience in transportation, automotive, or industrial automation domains. Knowledge of edge AI deployment, sensor fusion, or real-time analytics. Contributions to open-source AI/ML projects or published research. What We Offer Opportunity to shape the future of mobility through AI innovation. Access to Siemens global network of experts, labs, and digital platforms, flexible work arrangements, and continuous learning opportunities. A mission-driven environment focused on sustainability, safety, and digital transformation. Desired Skills: 9+ years of experience is required. Great Communication skills. Analytical and problem-solving skills

Job Overview (Primary Skills - GCP, Kubeflow, Python, Vertex.ai) As a Machine Learning Engineer, you will oversee the entire lifecycle of machine learning models. Your role involves collaborating with cross-functional teams, including data scientists, data engineers, software engineers, and DevOps specialists, to bridge the gap between experimental model development and reliable production systems. You will be responsible for automating ML pipelines, optimizing model training and serving, ensuring model governance, and maintaining the stability of deployed systems. This position requires a blend of experience in software engineering, data engineering, and machine learning systems, along with a strong understanding of DevOps practices to enable faster experimentation, consistent performance, and scalable ML operations. What You Will Do Work with data science leadership and stakeholders to understand business objectives, map the scope of work, and support colleagues in achieving technical deliverables. Invest in strong relationships with colleagues and build a successful followership around a common goal. Build and optimize ML pipelines for feature engineering, model training, and inference. Develop low-latency, high-throughput model endpoints for distributed environments. Maintain cloud infrastructure for ML workloads, including GPUs/TPUs, across platforms like GCP, AWS, or Azure Troubleshoot, debug, and validate ML systems for performance and reliability. Write and maintain automated tests (unit and integration). Supports discussions with Data Engineers to work on data collection, storage, and retrieval processes. Collaborate with Data Governance to identify data issues and propose data cleansing or enhancement solutions. Drive continuous improvement efforts in enhancing performance and providing increased functionality, including developing processes for automation. Skills You Will Need Group Work Lead: Ability to lead portions of pod iteratives; can clearly communicate priorities and play an effective technical support role for colleagues. Communication: Maintaining timely communication with management and stakeholders on project progress, issues, and concerns. Developing effective communication plans tailored to diverse audiences. Consultive Mindset: Go beyond just providing analytics and actively engage stakeholders to understand their challenges and goals. Ability to have a business-first viewpoint when developing solutions. Cloud & ML Ops: Expertise in managing cloud-based ML infrastructures (GCP, AWS, or Azure), coupled with DevOps practices, ensures seamless model deployment, scalability, and system reliability. This includes containerization, CI/CD pipelines, and infrastructure-as-code tools. Proficiency in programming languages such as Python, SQL, and Java. Who You Are 5+ years of industry experience working with machine learning tools and technologies. Familiarity with agile development frameworks and collaboration tools (e.g., JIRA, Confluence). Experience using Tensorflow, PyTorch, scikit-learn, Kubeflow, pandas and numpy. and frameworks like Ray, Dask preferred. Expertise in data engineering, object-oriented programming, and familiarity with microservices and cloud technologies. An ongoing learner who seeks out emerging technology and can influence others to think innovatively. Gets energized by fast-paced environments and capable of supporting multiple projects - can identify primary and secondary objectives, prioritize time, and communicate timelines to team members. Dedicated to fulfilling ideals of diversity, inclusion, and respect that the client aspire to achieve every day in every way. Regularly required to sit, talk, hear; use hands/fingers to touch, handle, and feel. Occasionally required to move about the workplace and reach with hands and arms. Requires close vision.

Roles and Responsibilities: Architecture & Infrastructure Design Architect scalable, resilient, and secure AI/ML infrastructure on AWS using services like EC2, SageMaker, Bedrock, VPC, RDS, DynamoDB, CloudWatch . Develop Infrastructure as Code (IaC) using Terraform , and automate deployments with CI/CD pipelines . Optimize cost and performance of cloud resources used for AI workloads. AI Project Leadership Translate business objectives into actionable AI strategies and solutions. Oversee the entire AI lifecycle from data ingestion, model training, and evaluation to deployment and monitoring. Drive roadmap planning, delivery timelines, and project success metrics. Model Development & Deployment Lead selection and development of AI/ML models, particularly for NLP, GenAI, and AIOps use cases . Implement frameworks for bias detection, explainability , and responsible AI . Enhance model performance through tuning and efficient resource utilization. Security & Compliance Ensure data privacy, security best practices, and compliance with IAM policies, encryption standards , and regulatory frameworks. Perform regular audits and vulnerability assessments to ensure system integrity. Team Leadership & Collaboration Lead and mentor a team of cloud engineers, ML practitioners, software developers, and data analysts. Promote cross-functional collaboration with business and technical stakeholders. Conduct technical reviews and ensure delivery of production-grade solutions. Monitoring & Maintenance Establish robust model monitoring , alerting , and feedback loops to detect drift and maintain model reliability. Ensure ongoing optimization of infrastructure and ML pipelines. Must-Have Skills: 10+ years of experience in IT with 4+ years in AI/ML leadership roles. Strong hands-on experience in AWS services : EC2, SageMaker, Bedrock, RDS, VPC, DynamoDB, CloudWatch. Expertise in Python for ML development and automation. Solid understanding of Terraform, Docker, Git , and CI/CD pipelines . Proven track record in delivering AI/ML projects into production environments . Deep understanding of MLOps, model versioning, monitoring , and retraining pipelines . Experience in implementing Responsible AI practices – including fairness, explainability, and bias mitigation. Knowledge of cloud security best practices and IAM role configuration. Excellent leadership, communication, and stakeholder management skills. Good-to-Have Skills: AWS Certifications such as AWS Certified Machine Learning – Specialty or AWS Certified Solutions Architect. Familiarity with data privacy laws and frameworks (GDPR, HIPAA). Experience with AI governance and ethical AI frameworks. Expertise in cost optimization and performance tuning for AI on the cloud. Exposure to LangChain , LLMs , Kubeflow , or GCP-based AI services .

Looking for MLOps Engineer to build and scale ML pipelines on AWS using SageMaker, EKS, Docker, and Terraform. Drive CI/CD, model tracking, automation & GPU-based training. Required Candidate profile Sagemaker, EKS, EC2, IAM, Cloudwatch, ECR, Docker, Kubernetes, Helm, Jenkins, Terraform, Kubeflow, Mlflow, Wandb Volcano Scheduler