AI / LLM Engineer

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.

• 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

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

• 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