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About the Role
Take ownership of the retrieval layer at the heart of a production AI platform, managing document ingestion, chunking, embeddings, hybrid search, reranking, evaluation, and agentic retrieval. This hands-on role offers significant influence over architecture and technical direction for building scalable and reliable AI retrieval systems.
Responsibilities
- Own document ingestion, chunking, and embeddings.
- Manage hybrid search, reranking, evaluation, and agentic retrieval.
- Build AI systems that retrieve information accurately and reliably at scale.
- Take genuine end-to-end ownership.
Requirements
- 5+ years of production backend engineering experience.
- 2+ years building and operating RAG / retrieval systems in production.
- Strong Python experience.
- Experience with FastAPI or similar production Python frameworks.
- Production experience with vector databases (e.g., Qdrant, Pinecone, Weaviate).
- Strong understanding of embeddings, semantic search, hybrid retrieval, and reranking.
- Experience evaluating retrieval quality using metrics like Recall@K and NDCG.
- Experience with PostgreSQL / SQL.
- Experience diagnosing and improving retrieval performance in production.
Skills
- Python
- FastAPI
- Vector databases
- Qdrant
- Pinecone
- Weaviate
- Embeddings
- Semantic search
- Hybrid retrieval
- Reranking
- Recall@K
- NDCG
- PostgreSQL
- SQL
- Agentic retrieval
- Multi-step search workflows
- Query decomposition
- BM25
- Information retrieval
- Golden datasets
- Retrieval regression testing
- Large-scale document ingestion
- OCR-heavy document ingestion
- Redis
- Background processing
- Multi-tenant architectures
- Data isolation
- Multilingual retrieval/search
- Benchmarking embedding models
- Benchmarking vector databases
- Benchmarking LLM approaches
- AI coding tools
- Claude Code
- Copilot
Location
- Remote (EU)
Work Type
- Fully remote
Experience Level
- Senior
- 5+ years backend engineering
- 2+ years RAG/retrieval systems
About the Company
- Fast-growing AI company.