SG
Senior Full Stack AI Engineer (RAG / LLM) [h31x7]
SKM Group
Remote · France Contract Senior €54k – €120k/yr 1w ago
About the role
About Us
We are a software house with 18 years of experience and a global portfolio of projects. We help businesses modernize, scale, and innovate through custom software solutions. Our team embraces unconventional ideas and new technologies, delivering solutions with real impact. If you value professionalism, creativity, and a strong engineering culture, you'll feel at home here.
What You’ll Do
- Build backend services in Python (FastAPI)
- Develop end-to-end RAG pipelines (ingestion → retrieval → generation)
- Implement hybrid search (vector + BM25) using Azure AI Search / Qdrant
- Process complex documents (PDF, OCR, structured chunking)
- Generate structured outputs (Excel/Word reports)
- Support internal tools with Streamlit and integrate with Node.js / React
Requirements
- Strong Python and backend engineering experience
- Hands‑on with RAG, embeddings, and LLMs (OpenAI APIs)
- Experience with vector databases and retrieval systems
- Familiarity with async processing (Celery, messaging)
- Basic Node.js / React for integration
Nice to Have
- Azure AI Search
- RAG evaluation frameworks
- LangChain / multi‑agent systems
What We Offer
- Attractive salary
- Large freedom and real influence
- No unhealthy competition, team approach to meeting challenges
- Remote‑first, flexible working culture
- Company apartments in cool cities across Europe: work and enjoy a memorable getaway
Job Details
- Job Type: Full‑time
- Pay: 54,000.00 € – 120,000.00 € per year
- Experience:
- Python: 5 years (required)
- AI: 3 years (required)
- Work Location: Remote
Requirements
- Strong Python and backend engineering experience
- Hands-on with RAG, embeddings, and LLMs (OpenAI APIs)
- Experience with vector databases and retrieval systems
- Familiarity with async processing (Celery, messaging)
- Basic Node.js / React for integration
Responsibilities
- Build backend services in Python (FastAPI)
- Develop end-to-end RAG pipelines (ingestion → retrieval → generation)
- Implement hybrid search (vector + BM25) using Azure AI Search / Qdrant
- Process complex documents (PDF, OCR, structured chunking)
- Generate structured outputs (Excel/Word reports)
- Support internal tools with Streamlit and integrate with Node.js / React
Skills
AWS LambdaAzure AI SearchCeleryFastAPILLMNode.jsOpenAI APIsOCRPythonQdrantRAGReactStreamlitVector databases
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