Back
Mid-level (3–6yr) applied AI role in Bangalore with broad agentic/ML skillset yields medium competition.
Highly specialized agentic AI, MCP, and evals expertise reduces cross-industry transferability.
Multiple mandatory filters: 3–6yrs ML experience, agentic systems, MCP, production evals, Python, and cloud.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own end-to-end execution of agentic AI roadmap including agent harness, tool design, evaluations, reliability, and user interactive experiences.
Design and build agentic workflows over MIA's MCP server with complex reasoning, session management, and error recovery for a production system.
Develop and maintain evaluation pipelines to measure agent quality and extend UI/tool surfaces for actionable agent outputs.
3–6 years of ML/AI engineering experience with hands-on work building and shipping agentic AI systems (not just RAG chatbots).
Experience with MCP (Model Context Protocol) or comparable tool-calling frameworks and understanding of agent-tool interaction failure modes.
Strong Python skills with production experience in LLM orchestration and building evaluation metrics for agent performance.
Work Experience Required: 3–6 years; Cloud deployment experience; Degree requirements: Not explicitly mentioned in the JD; Notice period: Not explicitly mentioned in the JD.
Experienced in production-level agentic AI systems used by real customers, able to bridge between technical implementation and business use cases.
Comfortable managing ambiguity and shipping prototypes and v1 solutions without fully specified specs.
Familiarity with agent orchestration tools like LangGraph or Claude Agent SDK and building robust evaluation/testing frameworks.