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python apis llm retrieval embeddings📋 Description
- Build scalable backend services and internal APIs for the AI platform.
- Integrate LLMs and retrieval into reliable, production-ready workflows.
- Build knowledge ingestion pipelines for LLMs (docs, APIs, data).
- Design chunking and embedding approaches with vector DB data models and indexing.
- Implement retrieval pipelines (semantic, keyword, hybrid) and caching.
- Contribute to shared infrastructure: CI/CD, observability, deployments.
🎯 Requirements
- 5+ years of experience in Python backend engineering and systems design experience.
- Experience shipping AI-powered or LLM-integrated backend systems.
- Experience with vector DBs (Qdrant/Pinecone/Chroma/etc.).
- Understanding of embeddings, chunking, and retrieval strategies.
- Experience building search or retrieval systems over unstructured data.
- Comfort working across multiple 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: Global team with MaintainX values.
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