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Senior Python Engineer – Vector Search / Retrieval Infrastructure

Startup Environment, High Ownership, Demanding Clients

Join our startup-like environment filled with new ideas, rapid change, and a strong focus on priorities and delivery.

We are looking for a Senior Python Engineer — someone experienced, open-minded, and ready to take real ownership of both product and technical direction. You will work closely with demanding clients, solve complex engineering problems, and help build and scale ambitious, high-performance vector search systems.

This role is for engineers who enjoy responsibility, fast feedback loops, architectural challenges, and working under pressure.

Your primary responsibilities beyond the will to change the world:

  • Design, implement, and optimize vector database solutions (Qdrant, Pinecone, Weaviate, or similar) for production-scale retrieval systems.

  • Build and maintain Python services for embedding generation, indexing, and semantic search pipelines.

  • Tune indexing strategies (HNSW, quantization, sharding) for latency, recall, and cost trade-offs.

  • Integrate vector search with RAG pipelines, LLM applications, or recommendation systems.

  • Collaborate with ML/data teams to optimize embedding models and retrieval quality.

  • Monitor, profile, and improve performance of vector search infrastructure at scale.

Requirements:

  • Strong Python skills, including experience with async programming and API design.

  • Hands-on experience with Qdrant, Pinecone, Weaviate, Milvus, or a comparable vector database.

  • Solid understanding of embedding models, similarity search, and ANN algorithms (HNSW, IVF, etc.).

  • Experience with Docker/Kubernetes and cloud deployment (AWS/GCP/Azure).

  • Familiarity with RAG architectures and LLM-based applications is a plus.

  • Comfortable working with large-scale data pipelines and performance optimization.

Nice to Have:

  • Experience with other DB technologies (PostgreSQL + pgvector, Redis, Elasticsearch).

  • Background in ML/NLP.

  • Contributions to open-source vector search projects.

Benefits

The list of benefits is long, so we'll mention only the crucial ones:

  • Challenging projects with real business impact

  • Work in a startup-like environment: fast decisions, autonomy, responsibility

  • 100% remote or office in Poland (Wrocław / Zielona Góra)

  • Sport subscription

  • Private healthcare

See also

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