Senior Information Retrieval Engineer, Brand Concierge

What you'll do

• Architect and deploy scalable retrieval pipelines using vector databases like FAISS, Weaviate, or Pinecone.
• Build ingestion pipelines for structured and unstructured data including chunking, embedding generation, and metadata tagging.
• Fine-tune relevance scoring, reranking algorithms, and query understanding mechanisms to improve precision and recall.
• Create and maintain knowledge graphs to support context linking and data disambiguation.
• Design context window strategies to improve LLM reasoning through adaptive injection and task-based retrieval.
• Track key retrieval metrics such as accuracy, latency, and fallback rates to monitor system performance.
• Implement caching, prefetching, and deduplication strategies to optimize system responsiveness.

What we're looking for

• 4+ years of experience in data engineering, ML infrastructure, or information retrieval.
• Experience building and deploying RAG pipelines or semantic search systems.
• Strong ML and Python skills with familiarity with retrieval libraries like Haystack, LangChain, Elasticsearch, or Milvus.
• Proficiency with embedding models, vector similarity search, and document indexing.
• Familiarity with cloud platforms and MLOps tooling such as Airflow, dbt, and Docker.
• Knowledge of graph databases or knowledge graph design (preferred).
• Experience optimizing retrieval for LLMs such as OpenAI, Anthropic, or Mistral (preferred).
• Background in IR/NLP, Search Engineering, or Cognitive Computing (preferred).
• Degree in Computer Science, Information Systems, or a related field (preferred).