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Metro Bangalore and reputable financial brand increase applicants, but seniority and niche agentic AI skills moderate competition.
Agentic AI and MLOps skills transfer across industries, but platform and regulated-finance experience increase domain bias.
Requires specialized MLOps, agentic AI, Python, Kubernetes skills, enforcing strict technical filters.
Own and deliver the CSP agent interoperability capability, including shared APIs, Model Context Protocol (MCP) servers, reusable agents, and agent-to-agent interaction patterns.
Drive technical alignment, architecture consistency, and enforce engineering standards across multiple squads focused on agentic AI and ML-enabled services.
Lead complex cross-team technical problem solving, mentor senior engineers, and support scalable, secure, and compliant AI/ML platform solutions.
Proven senior engineering experience operating across multiple squads or a domain.
Strong MLOps engineering skills including CI/CD for ML, model lifecycle, and production operations.
Hands-on with Python, secure API and distributed service engineering, authentication, authorisation, observability.
Experience with containerization and cloud platforms such as Docker, Kubernetes, and AWS. Work Experience Required: Not explicitly mentioned in the JD.
Experienced in architecting and delivering AI/ML platform components focused on agentic AI or similar AI tool integration protocols like MCP.
Comfortable collaborating with product, architecture, and engineering leadership to influence roadmap and technical direction.
Demonstrated capability mentoring senior engineers and contributing to engineering standards, with a strong focus on non-functional requirements like security, resilience, and scalability.