AI/ LLM Engineer
Indexed description
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.
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
- 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
- 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
If you like figuring things out and shipping them — you'll fit in.
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
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