Required Experience, minimum: 4 years

Required Experience, maximum: 8 years

- Skills: AWS, ML frameworks, Pytorch, CI/CD Integration, Python
- Minimum Qualification: B.Tech/B.E

Job Profile

You will be responsible for designing, building, and operating scalable MLOps and LLMOps platforms on AWS , enabling Machine Learning, GenAI, and Agentic AI solutions to move reliably from development into production.

- Design and implement end-to-end MLOps and LLMOps pipelines on AWS.
- Build automated pipelines for model/LLM training, evaluation, versioning, deployment, monitoring, and rollback.
- Develop scalable deployment patterns for ML models, LLM applications, RAG and Agentic AI solutions.
- Build and maintain CI/CD pipelines for ML and GenAI workloads.
- Develop reusable Infrastructure as Code (IaC) components for cloud infrastructure and AI platforms.
- Containerize and orchestrate ML/LLM workloads using Docker and Kubernetes/EKS.
- Implement orchestration workflows using Argo Workflows and related cloud-native technologies.
- Establish production monitoring and observability for models and LLM applications, covering latency, quality, failures, resource utilization, and cost.
- Enable secure integration of foundation models and GenAI services using Amazon Bedrock and related AWS services.
- Work closely with Data Scientists, AI Engineers, Software Engineers, Cloud/DevOps teams, and architecture stakeholders to industrialize AI solutions.




- Support production operations, troubleshooting, scalability, reliability, security, and continuous improvement of AI services.

Education

- University degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related technical field.
- Ideally specialization or hands-on project experience in Machine Learning, Cloud Engineering, or Generative AI.

Experience

- Solid hands-on experience in MLOps, DevOps, Cloud Engineering, or ML Platform Engineering.
- Practical experience covering the complete ML production lifecycle:
- Data and feature pipelines
- Model training and experiment management
- Model packaging and versioning
- Automated deployment
- Evaluation and monitoring
- Production operations and rollback

- Hands-on experience deploying and operating ML solutions on AWS.
- Experience implementing CI/CD and Infrastructure as Code for production environments.
- Experience operating containerized workloads using Docker and Kubernetes.
- Experience deploying or operating LLM/GenAI applications in production is highly desirable.
- Experience with RAG, LLM evaluation, prompt/model versioning or Agentic AI systems is an advantage.
- Experience working in enterprise-grade or regulated production environments is preferred.
- Automotive software development experience is an advantage.

📌 ML Ops and LLM Ops -AWS Engineer AIML (Pune)
🏢 BMW TechWorks India
📍 Pune