About the role

You build the AI agents at the heart of M-agentic — Agentic Process Studio. Working within the agent squad, you implement and test the agents that automate a real Health Insurance Claim Adjudication process on AWS AgentCore + Amazon Bedrock (Claude) — writing the prompts, tools, guardrails and integrations that make each agent reliable, protected and auditable.
Key responsibilities
Implement worker and application agents on AWS AgentCore / Bedrock: prompts, tool definitions, control flow and hand-offs between steps.
Engineer and iterate prompts for the Studio's AI features (intake parsing, gap analysis, agent-blueprint generation) and contribute test cases to the eval harness.
Integrate enterprise systems as governed tools (API connectors / MCP tools) for the six claims systems (Availity, HealthEdge, Optum, Shift, Zelis, InterQual) using sandbox adapters.
Wire safety and compliance into every agent: Bedrock Guardrails, PHI masking, and the immutable audit trail.
Write clean, tested Python; participate in code review and pair with the Lead and peers.
Help implement the human-in-the-loop gates (clinical review, SIU) and the Forge deploy path.
Debug agent behaviour using traces,



logs and evals; tune model choice and cost per step.
Required qualifications
2+ years in software or ML engineering with robust Python.
Practical experience with LLM applications — prompt engineering, tool/function calling, and at least one agent or RAG project (professional, open-source or substantial personal).
Working knowledge of AWS (Lambda, IAM, S3, and event-driven basics).
Solid fundamentals: version control, testing, APIs, JSON/data handling.
Curious, detail-oriented, and comfortable with rapid iteration and measurement.
Preferred
Exposure to Amazon Bedrock, AWS AgentCore, Bedrock Agents, LangGraph/LangChain, or MCP.
Experience integrating third-party APIs or building connectors.
Interest in AI safety, guardrails, evals, or regulated-data handling.
What success looks like (first 8 weeks)
Owns one or more worker agents end-to-end — built, tested and running in the pipeline.
Contributes reliable prompts and eval cases that raise output quality measurably.
Ships steadily within the squad's cadence with clean, reviewed code

📌 Gen Ai Developer / Lead / Architect Hyderabad
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📍 Hyderabad