## 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 strong **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 fast 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)
🏢 Perfect Job Accord
📍 Hyderabad