LLM-based features You will play a key role in advancing their microservices-based, event-driven architecture. What youll bring: 3+ years of commercial software engineering experience with Python (FastAPI/Flask) and React. Experience with cloud platforms (GCP/AWS/Azure) and Elasticsearch. Familiarity with CI/CD and infrastructure-as-code tools like Docker, GitHub Actions, or More ❯
play a part in shaping the technical vision of this business and work on their product from an early stage. What you'll work on: Backend APIs (Python/FastAPI): Build and maintain secure, high-performance services that drive AI features and data access at scale. RAG & vector search: Design and improve retrieval pipelines (embeddings, chunking, hybrid search, ranking, feedback … pgvector, FAISS, Weaviate, Elasticsearch k-NN), embeddings, and hybrid search methods. Practical knowledge of chunking strategies, indexing, precision/recall trade-offs, reranking, and evaluation techniques. Proficient in Python (FastAPI) and React/Next.js . Solid experience with both SQL and NoSQL databases (Postgres, DynamoDB, etc.). Exposure to LLMs, embeddings, and vector search APIs . Strong understanding of data More ❯
play a part in shaping the technical vision of this business and work on their product from an early stage. What you'll work on: Backend APIs (Python/FastAPI): Build and maintain secure, high-performance services that drive AI features and data access at scale. RAG & vector search: Design and improve retrieval pipelines (embeddings, chunking, hybrid search, ranking, feedback … pgvector, FAISS, Weaviate, Elasticsearch k-NN), embeddings, and hybrid search methods. Practical knowledge of chunking strategies, indexing, precision/recall trade-offs, reranking, and evaluation techniques. Proficient in Python (FastAPI) and React/Next.js . Solid experience with both SQL and NoSQL databases (Postgres, DynamoDB, etc.). Exposure to LLMs, embeddings, and vector search APIs . Strong understanding of data More ❯
