AI LLM Retrieval Engineer

Proven track record building and optimizing retrieval systems in production Deep understanding of embedding models, indexing strategies, and ANN search Experience with vector DBs (e.g., Pinecone, Weaviate, Milvus, Vespa, FAISS) Strong proficiency in Python and familiarity with RAG frameworks (LangChain, LlamaIndex, Haystack) Knowledge of relevance tuning, hybrid search, and retrieval evaluation Experience working with graph databases (Neo4J, ArangoDB) Strong communication skills for technical and client-facing collaboration

Architect and maintain client-specific and internal RAG pipelines, including embedding generation, document chunking, and metadata tagging Collaborate directly with enterprise clients to assess data landscapes, retrieval requirements, and grounding needs Select, test, and optimize embedding models (text, multimodal) for accuracy and efficiency Design retrieval strategies (dense, sparse, hybrid) to maximize precision and recall Establish and monitor evaluation metrics for retrieval performance (e.g., recall@k, MRR, nDCG) Align retrieval structure, granularity, and metadata with reasoning workflows Maintain documentation, schemas, and retrieval guidelines for internal and client teams Stay current with advancements in vector databases, embedding models, and retrieval frameworks