Key Skills

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

Core Responsibilities

Design and deploy production-grade AI agents and stateful workflows using Python and LangGraph. Integrate LLMs with enterprise systems, APIs, and databases to create reliable, multi-step agentic applications.

Requirements

Requires strong commercial Python experience and hands-on expertise with LangGraph and LLM-powered applications. Candidates must be proficient in production engineering, including API development, CI/CD, and cloud platforms.