and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic AI adoption roadmap and More ❯
on engineer with an ownership mindset, strong communication skills, and a collaborative approach. 5+ years experience in full-stack development. Strong background in RAG systems , vector databases (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/ More ❯
on engineer with an ownership mindset, strong communication skills, and a collaborative approach. 5+ years experience in full-stack development. Strong background in RAG systems , vector databases (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/ More ❯
