Belmont Lavan Ltd
AI Application Engineer - LangGraph & Agentic AI
Get notified of more jobs like this · No spam, ever
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
About the Role
We are looking for an experienced AI Application Engineer to design and build intelligent applications powered by LLMs, LangGraph, and modern agentic AI technologies.
You will focus on transforming business requirements into AI applications capable of reasoning through tasks, retrieving information, interacting with tools and enterprise systems, requesting human approval when required, and completing business processes.
This role sits at the intersection of AI engineering, software development, workflow automation, and business process transformation.
Responsibilities
Agentic AI Application Development
- Design and develop AI applications using LangGraph and LLM technologies.
- Build agents capable of executing complex, multi-step business processes.
- Design stateful workflows incorporating reasoning, tool usage, validation, approvals, and exception handling.
- Develop single-agent and multi-agent solutions where appropriate.
- Translate business requirements into practical agentic AI architectures.
LLM Application Engineering
- Integrate LLMs into production applications.
- Develop prompt strategies, structured outputs, tool calling, and context-management approaches.
- Select appropriate models based on accuracy, capability, latency, security, and cost.
- Develop mechanisms to improve reliability and reduce hallucinations.
- Implement appropriate guardrails around AI-generated decisions and actions.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
RAG and Enterprise Knowledge
- Design and implement Retrieval-Augmented Generation (RAG) solutions.
- Connect AI applications to enterprise documents, databases, APIs, and knowledge repositories.
- Develop retrieval and ranking strategies to provide agents with relevant context.
- Work with embeddings and vector databases.
- Implement data and context pipelines supporting AI agents.
Business Process Automation
- Analyse business processes and identify opportunities for agentic automation.
- Design AI workflows that combine LLM reasoning with deterministic business logic.
- Build agents capable of retrieving information, making decisions, invoking tools, and completing actions.
- Implement human-in-the-loop approval and escalation processes.
- Ensure automated actions are controlled, auditable, and reversible where appropriate.
Evaluation and Quality
- Develop evaluation frameworks for AI applications and agent workflows.
- Define metrics covering accuracy, task completion, reliability, latency, and cost.
- Build automated tests for prompts, agents, tools, and end-to-end workflows.
- Analyse failures and continuously improve agent behaviour.
- Use observability and evaluation data to optimise production systems.
Production Deployment
- Deploy and operate AI applications in cloud and enterprise environments.
- Implement monitoring, logging, tracing, and performance management.
- Design resilient workflows with retries, timeouts, fallbacks, and recovery mechanisms.
- Work with DevOps and platform teams to establish appropriate deployment and CI/CD practices.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Cross-Functional Collaboration
- Work closely with product managers, business analysts, software engineers, data scientists, architects, and business stakeholders.
- Communicate AI capabilities, limitations, risks, and implementation options.
- Help organisations identify realistic and valuable use cases for agentic AI.
Required Experience
- Commercial experience developing AI/LLM applications.
- Hands-on experience with LangGraph and agentic workflow development.
- Strong Python development experience.
- Experience deploying AI applications into production.
- Strong understanding of LLMs, RAG, tool calling, structured outputs, and prompt engineering.
- Experience integrating AI applications with APIs, databases, enterprise systems, or SaaS platforms.
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Experience with AI evaluation, monitoring, and observability.
Desirable Skills
- LangChain / LangSmith
- Multi-agent systems
- AI workflow orchestration
- Vector databases
- Kubernetes
- Docker
- FastAPI
- Data pipelines
- MLOps
- AI security and governance
- Enterprise process automation
- Experience with financial services, healthcare, retail, manufacturing, or other complex enterprise environments
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Location