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Metro Bangalore posting with broad MLOps skillset requirements yields medium candidate competition.
Requires ML lifecycle, model deployment, and ML-specific tooling, so background fit is highly domain-sensitive.
Multiple mandatory ML, cloud, containerization, orchestration and data pipeline skills increase shortlisting strictness to high.
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Build and support scalable ML and MLOps systems including model training, deployment, and inference using microservices and containerized environments.
Develop, deploy, and maintain ML solutions as modular microservices with APIs, leveraging Docker and Kubernetes for scalability and reliability.
Monitor production ML systems for performance, latency, drift, and build CI/CD pipelines to automate testing and deployment of ML workflows.
Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.
Experience with ML lifecycle management tools (e.g., AWS SageMaker, Azure ML, Dataiku) and deploying ML models as microservices using frameworks like FastAPI or Flask.
Hands-on experience with Docker containerization and Kubernetes orchestration.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building scalable, production-ready ML infrastructure integrating multiple teams (engineering, data science, business).
Proficient with distributed ML systems, multi-agent or LLM-based agent solutions, and data processing frameworks like Spark or Hadoop.
Skilled in cloud platforms AWS or Azure for deploying scalable ML solutions with strong programming skills in Python and SQL.