Python Engineer - LangGraph & AI Agents

Job description

We are looking for a Python Engineer with hands-on LangGraph experience to build and deploy production-grade AI agents and agentic workflows.

You will combine strong Python software engineering with modern LLM technologies to develop AI systems that can execute multi-step tasks, interact with business systems, use external tools, retrieve information, and operate reliably in production environments.

This is a hands-on engineering role for someone who enjoys solving complex software problems and has experience taking AI/LLM solutions beyond prototypes into production.

Requirements

Python & AI Agent Development - Design, develop, test, and maintain AI agent applications using Python and LangGraph . - Build stateful, multi-step agent workflows with branching, looping, retries, and error handling. - Implement tool calling and integrations that allow agents to interact with APIs, databases, and enterprise systems. - Develop reusable components and frameworks for agentic applications. - Integrate LLMs into robust software applications rather than treating them as standalone chat interfaces.

LangGraph Engineering - Build and maintain LangGraph-based workflows and agents . - Implement state management, persistence, checkpoints, and workflow recovery. - Develop human-in-the-loop workflows and approval mechanisms. - Design appropriate single-agent and multi-agent architectures. - Optimise agent workflows for reliability, latency, scalability, and cost.

Production Engineering - Deploy AI applications into production environments. - Build APIs and services around AI agents. - Implement testing, logging, monitoring, tracing, and error handling. - Troubleshoot production issues and improve application reliability. - Contribute to CI/CD pipelines and automated deployment processes.

LLM and RAG Integration - Integrate commercial and open-source LLMs into production applications. - Implement prompt templates, structured outputs, function/tool calling, and context management. - Develop RAG solutions using enterprise data sources. - Work with embeddings and vector databases where appropriate. - Evaluate model performance and optimise model selection, latency, and cost.

Enterprise Integration - Integrate AI agents with REST APIs, databases, SaaS platforms, and internal business systems. - Develop secure tools and interfaces for agents to perform business actions. - Implement appropriate authentication, authorisation, validation, and access controls. - Ensure agent actions are auditable and appropriately controlled.

Required Experience - Strong commercial experience with Python . - Hands-on experience developing applications using LangGraph . - Experience building and deploying LLM-powered applications or AI agents . - Experience developing production APIs and backend services. - Strong understanding of software engineering principles, testing, version control, and CI/CD. - Experience with REST APIs and enterprise system integration. - Understanding of LLM concepts including prompting, tool calling, structured output, embeddings, and RAG. - Experience deploying applications on AWS, Azure, or GCP .

Desirable Skills - LangChain / LangSmith - Multi-agent architectures - Vector databases - Kubernetes and Docker - Infrastructure as Code - Event-driven architectures - AI observability and evaluation - AI security and guardrails - PostgreSQL or other relational databases - Redis or similar caching technologies