MLOps Engineer III
HybridBengaluru, Karnataka, India
Job Summary
Design and build the central MLOps/LLMOps Platform covering the full lifecycle of classical ML and Agentic/GenAI workloads. Architect robust CI/CD workflows and self-service capabilities that empower Data Scientists and ML Engineers to deploy models and agents independently. Build and evolve the Agentic Platform on AWS Bedrock, utilizing frameworks such as LangGraph or comparable tools. Collaborate with Data Scientists, ML Engineers, and Infrastructure teams to translate requirements into scalable platform capabilities. Establish and promote MLOps and LLMOps best practices through training, documentation, and hands-on support. Optimize infrastructure costs and observability by implementing efficient resource management and intelligent automation. Work in a high-ownership environment where contributions shape how AI is built and deployed across the organization.
Required Qualifications
- Bachelor's Engineering or Master's degree in Computer Science or a related discipline
- atleast 7 years of experience in MLOps, LLMOps, ML Engineering, or DevOps
- proven track record delivering production ML or GenAI platforms
- hands-on expertise with AWS SageMaker
- hands-on expertise with AWS Bedrock
- hands-on expertise with LangGraph
- hands-on expertise with LangChain
- strong skills in containerisation (Docker, Kubernetes)
- strong skills in CI/CD tooling
- strong skills in modern monitoring solutions
- Deep understanding of ML and LLM lifecycles
- ability to design scalable, self-service platform capabilities that serve diverse engineering teams
- driven platform adoption across multiple teams
- thrive on fostering change, automation, and innovation in fast-moving environments
- excell at explaining complex technical topics to diverse audiences
- influencing decision-making in an international environment
- solution-oriented
- continuously learning
- keep up to date with the latest trends in ML, LLMs, and Agentic AI
- bring that knowledge into your day-to-day work
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