AI Application Engineer - LangGraph & Agentic AI
Belmont Lavan Ltd · Francescas, France
What this role requires
5 requirements, read out of the advert rather than guessed from the job title:
Also mentioned, not required: LLM, Kubernetes, Docker, FastAPI, Data pipelines, MLOps, AI security and governance, Enterprise process automation, English (B2). Worth having, but their absence is not what gets a CV filtered out.
Free, no card. Tells you which of them your CV evidences and which it only implies.
Job description
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 .
Requirements
Agentic AI Application Development
Design and develop AI applications using LangGraph and LLM technologies .
Read the full description (60 more sections)Show less
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.
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.
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
Find more English Speaking Jobs in France on Arbeitnow
You will apply. Then you will hear nothing.
And no one will tell you what was wrong. See it before you send: your ATS score, every weak line, and the fix for each.
- 1
Drop in your CV
One PDF, thirty seconds. No card.
- 2
See what is wrong with it
Every weak passage, quoted from your own CV, with the line to replace it.
- 3
Apply where you fit
Every European role ranked against what your CV actually says.
- Free, no card
- Your CV file is deleted after parsing
- Or skip the upload — build your profile by hand
- Refreshed every 6 hours