AI/LLM Engineer

Summary: Build and deploy LLM-powered applications, RAG systems, and AI agents for international clients in banking, insurance, and telco sectors. The role involves designing production-grade Python code, integrating LLMs, and collaborating directly with clients to translate business problems into working AI solutions.

### About the role

We're looking for an engineer who genuinely gets excited about building things with AI — someone who shipped something real with LLMs, not just played with the API. You'll work on international projects for clients in banking, insurance, and telco (US, Netherlands, UK), translating messy business problems into working AI systems.

You don't need 10 years of "AI experience" — this field is too new for that. What matters is that you think clearly, learn fast, and can take an idea from whiteboard to production.

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### What you'll be doing

• Building LLM-powered applications and RAG systems for enterprise clients
• Designing and implementing AI agents (LangChain, LangGraph, CrewAI or similar)
• Integrating LLMs (OpenAI, Anthropic, Gemini, open-source) into existing systems
• Building data ingestion pipelines: chunking, embedding, vector indexing
• Writing production-grade Python code — APIs, tests, containers, the full stack
• Working directly with clients: understanding their requirements, presenting solutions
• Doing code reviews, writing docs, contributing to team engineering standards
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### What we're looking for

• You've built something with LLMs that actually runs in production — doesn't matter if it was at a big company or a side project
• Solid Python skills — not scripts, but clean code you're not ashamed of
• Working knowledge of RAG: you understand why naive chunking fails and what to do about it
• Experience with at least one agent framework (LangChain, LlamaIndex, LangGraph)
• Familiarity with vector databases (Qdrant, Weaviate, Pinecone, pgvector — any)
• You know how to ship: REST APIs, Docker, cloud basics (AWS / Azure / GCP)
• English B2+ — client-facing role, calls and written communication included
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### Nice to have

• Experience evaluating LLM outputs (RAGAS, LangSmith, Arize or similar)
• MLflow or another experiment tracking tool
• Databricks, Azure AI Foundry or AWS Bedrock
• Fine-tuning experience (LoRA, PEFT, anything hands-on)
• Kafka or streaming pipelines for real-time AI use cases
• n8n
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### About Devapo

We provide software engineering and data specialists to international clients who want people, not vendors. Our engineers work embedded in client teams on real problems — not slide decks. We invest in your growth: certifications, learning time, architecture access, and honest feedback from people who know what they're talking about.

If you like figuring things out and shipping them — you'll fit in.

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### What we offer

• Certifications and training funded
• Private medical care (Medicover)
• Multisport card
• English language classes
• Flexible working hours
• Team meetups and integration events
• Referral bonus