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