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Metro location, broad MLOps skillset, and known global employer create moderate candidate competition.
Requires ML-specific operational skills, reasonably transferable across industries but with ML domain sensitivity.
Extensive mandatory tech stack (Kubernetes, Docker, AWS/SageMaker, CI/CD, Spark) implies high shortlisting strictness.
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Design, build, and support scalable Machine Learning infrastructure enabling model training, deployment, and real-time inference.
Deploy ML models as containerized microservices using Docker and Kubernetes ensuring scalability, portability, and system reliability.
Monitor production ML systems for performance, drift, and latency; build CI/CD pipelines and maintain data pipelines for batch and real-time processing.
Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.
Experience with ML lifecycle management platforms like AWS SageMaker, Azure ML, or Dataiku.
Hands-on experience with Docker, Kubernetes, RESTful API development (FastAPI, Flask), and cloud platforms AWS or Azure.
Proficiency in Python and SQL; Work Experience Required: Not explicitly mentioned in the JD.
Experience building and deploying production-grade ML solutions in microservice/containerized environments.
Familiarity with multi-agent systems or LLM-based agents and distributed AI architectures is a strong advantage.
Comfortable working cross-functionally with data science, engineering, and business teams to deliver production-ready ML solutions.