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Metro Bengaluru location and broad MLOps skillset create moderate applicant competition.
MLOps platform skills transfer across industries but require specific ML and platform expertise.
Explicit 7+ years plus mandatory Bedrock/SageMaker, Kubernetes, and CI/CD experience makes filtering strict.
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Design and build the central MLOps/LLMOps platform supporting classical ML and next-gen Agentic/GenAI workloads at scale.
Architect CI/CD workflows and self-service capabilities enabling Data Scientists and ML Engineers to deploy models and agents independently.
Optimize platform infrastructure costs and observability through efficient resource management and automation on AWS Bedrock using tools like LangGraph.
Bachelor's or Master's degree in Computer Science or related discipline.
At least 7 years of experience in MLOps, LLMOps, ML Engineering, or DevOps with delivery of production ML or GenAI platforms.
Hands-on expertise with AWS SageMaker, AWS Bedrock, and AI frameworks such as LangGraph or LangChain; strong skills in containerization (Docker, Kubernetes), CI/CD tooling, and monitoring solutions.
Deep understanding of ML and LLM lifecycles and designing scalable, self-service platform capabilities.
Experienced MLOps/LLMOps engineer with a proven track record driving platform adoption across multiple teams in large-scale environments.
Technical strategic thinker skilled at collaborating across Data Science, ML Engineering, and Infrastructure teams to translate complex requirements into scalable platform features.
Proactive in applying latest ML, LLM, and Agentic AI trends into platform design and promoting best practices via documentation and training.