Related skills
python llm retrieval embeddings pinecone📋 Description
- Build scalable backend services and APIs for the AI platform.
- Integrate LLMs and retrieval into production-grade workflows.
- Build knowledge ingestion pipelines for LLMs (docs, APIs, semi-structured data).
- Design chunking, embeddings, and vector DB indexing strategies.
- Implement retrieval pipelines (semantic, keyword, hybrid) and caching.
- Contribute to CI/CD, observability, and deployments infrastructure.
🎯 Requirements
- 5+ years in Python backend engineering and systems design.
- Shipping AI-powered or LLM-integrated backend systems.
- Experience with vector databases (Qdrant, Pinecone, Chroma).
- Understanding embeddings, chunking, and retrieval strategies.
- Experience building search or retrieval systems over unstructured data.
- Comfort across layers: services, data, infra, AI tooling.
🎁 Benefits
- Competitive salary and meaningful equity opportunities.
- Healthcare, dental, and vision coverage.
- 401(k) / RRSP enrollment program.
- Take what you need PTO.
- Culture of meritocracy with globally distributed teammates.
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