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: [email protected]