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Senior Python Engineer (with LangGraph)
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Senior Python Engineer
We are looking for a Senior Python Engineer to design and build enterprise-grade multi-agent AI systems on AWS. The role focuses on LangGraph-based orchestration, agent collaboration patterns, stateful workflow execution, human-in-the-loop processes, and scalable agent runtime architectures. The ideal candidate has deep Python expertise, hands-on LangGraph experience, and a strong understanding of distributed workflow orchestration.
Project Overview
Our customer is a multinational corporation with more than a century of history and offices in over 180 countries. Their most ambitious goal at the time is to introduce a range of Reduced-Risk Products (RRPs). The target audience is more than 1 billion consumers around the globe. IT platform hosts 700+ applications.
The Platform is a set of services and internet applications that accelerate the development and delivery of software applications by taking care of common SDLC challenges. The Platform provides access and consumption for engineering teams to a set of services, technologies, practices for their development and for operating their application, ensuring a set of compliance and best practices.
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Requirements
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- Python (LangGraph, LangChain)
- AWS Bedrock AgentCore Runtime
- Multi-agent orchestration patterns (supervisor/worker, fan-out/fan-in)
- LangGraph checkpointing and state management
- Human-in-the-loop (HITL) workflow design
- A2A agent delegation via Agent Gateway
Experience
- 5+ years Python engineering
- LangGraph hands-on for multi-step agent workflows
- Workflow orchestration (Temporal, Airflow, or equivalent)
- Stateful distributed system patterns (checkpointing, idempotency)
Will Be a Plus
- AWS AgentCore Runtime maturity evaluation experience
- AWS Bedrock Memory API
- Enterprise HITL approval workflow integration
Responsibilities
- Design and develop multi-agent applications using Python, LangGraph, and LangChain.
- Build and maintain agent workflows leveraging AWS Bedrock AgentCore Runtime.
- Implement supervisor/worker, fan-out/fan-in, and other advanced agent orchestration patterns.
- Design stateful workflow execution models using LangGraph checkpointing and recovery mechanisms.
- Develop resilient and idempotent workflow execution strategies for long-running agent processes.
- Integrate Human-in-the-Loop (HITL) approval workflows into business-critical agent operations.
- Enable agent delegation and collaboration through Agent Gateway and A2A communication patterns.
- Optimize workflow reliability, performance, and scalability in distributed environments.
- Integrate memory, context management, and persistence capabilities into multi-agent systems.
- Collaborate with platform, security, and product teams to design production-ready AI solutions.
- Establish engineering standards, testing approaches, and operational best practices for agent-based systems.
- Support deployment, monitoring, and continuous improvement of AI workflows in AWS environments.
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