Belmont Lavan Ltd
Python Engineer - LangGraph & AI Agents
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Python Engineer with LangGraph Experience
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.
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.
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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.
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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
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