# Python Engineer - LangGraph & AI Agents

**Company:** [Belmont Lavan Ltd](null/companies/6heGsPm3cuBbCjvDWApsdc.md)
**Location:** Remote
**Workplace:** remote
**Employment type:** Full-time

[Apply for this job](null/view/10938598-4cd6-4bcf-a8d1-02025fa5e57a)

## 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