Python Engineer - LangGraph & AI Agents

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