AgentCore Developer

## The Role We are hiring an **AgentCore Developer** to build, deploy, and operate production AI agents on **Amazon Bedrock AgentCore**. You will own agent workloads end-to-end — from Runtime and Gateway configuration to Memory, Identity, and Observability — for client engagements that typically begin as a Proof Sprint and graduate into embedded retainers. This is a hands-on builder role for someone who has moved past prototypes and wants to run agents that serve real users under real SLAs. ## What You’ll Do Design and deploy agents on **AgentCore Runtime** (serverless microVM sessions, isolated execution, auto-scaling). Build tool layers using **AgentCore Gateway** — wrapping Lambda functions, OpenAPI specs, and internal APIs into MCP-compatible tools. Implement **AgentCore Memory** for persistent, personalized agent context across sessions. Configure **AgentCore Identity** for secure auth to AWS services and third-party systems (Okta, Entra, Cognito, Slack, Zoom). Instrument agents with **AgentCore Observability** (OpenTelemetry traces, dashboards, quality metrics) and integrate with our Magpie governance layer. Use the **AgentCore CLI** and **managed agent harness** for rapid iteration; drop into Strands-based code when custom orchestration is required. Build multi-agent systems using **LangGraph, CrewAI, LlamaIndex, or Strands Agents** — framework choice driven by the problem, not ideology. Ship Code Interpreter and Browser tool integrations for agents that execute code and drive web workflows. Own the full lifecycle: prototype → evals → deployment → monitoring → iteration. No throwing work over a wall. Work directly with US clients during overlap hours — discovery, scoping, demos, and delivery. ## Required Skills **3+ years** building production backend systems in Python (FastAPI or Django preferred). **Hands-on AgentCore experience** — Runtime, Gateway, and at least one of Memory/Identity/Observability in a deployed project. Strong working knowledge of at least one agent framework: **Strands, LangGraph, CrewAI, or LlamaIndex**. Deep familiarity with **MCP (Model Context Protocol)** — building servers, exposing tools, handling auth flows. AWS fluency: **Bedrock, Lambda, ECR, IAM, CloudWatch, CDK or Terraform**. Practical experience with **LLM evals** — building golden sets, measuring regression, defining quality gates. Understanding of agent failure modes: context window management, tool-call loops, hallucinated tool args, retry semantics. Solid grasp of containerization (Docker) and serverless deployment patterns. ## Nice to Have Experience with **fine-tuned SLMs** (Qwen, Llama, Mistral) and self-hosted inference (vLLM, Ollama). Prior work on **HIPAA, SOC 2, or EU AI Act** compliant AI systems. Contributions to agent frameworks or MCP ecosystem. Exposure to **LangSmith, Langfuse**, or equivalent observability stacks. Comfort with US client communication — discovery calls, written updates, live demos. ## What We Offer Direct work on AgentCore deployments for named US enterprise and healthcare clients. Ownership of agent systems end-to-end — no ticket-shuffling, no design-by-committee. Fast promotion path; senior engineers graduate into FDE lead and tech lead roles within 12–18 months. Competitive compensation with performance-linked bonuses tied to client retention. Hybrid work from our Pune office with flexible hours for US overlap. Learning budget for AWS certifications, conferences (re:Invent, AI Engineer Summit), and books. ## How to Apply Send your resume + link to an agent system you built — GitHub repo, writeup, or short Loom walkthrough. We weigh what you’ve shipped more than where you’ve **Apply to:** shivanyshre@steinnlabs.com