About the job
Join our team as an Information Retrieval Engineer and lead the development of cutting-edge retrieval systems for AI applications. If you have a passion for data engineering and machine learning, this is your chance to make a significant impact!
Responsibilities
Architect and deploy scalable retrieval pipelines using vector databases (e.g., FAISS, Weaviate, Pinecone, Qdrant); Implement semantic search infrastructure and hybrid retrieval systems (semantic + keyword); Build ingestion pipelines for both structured and unstructured data sources; Implement document chunking strategies, embedding generation (e.g., OpenAI, Cohere, HuggingFace), and metadata tagging; Fine-tune relevance scoring, reranking algorithms, and query understanding mechanisms; Develop techniques to improve precision/recall for specific business domains or user tasks; Create and maintain knowledge graphs to support context linking and disambiguation; Manage data freshness and version control to ensure consistency and reliability of retrieved content; Design and iterate on context window strategies that improve LLM reasoning (e.g., adaptive injection, task-based retrieval); Collaborate with prompt engineers and model developers to align retrieval outputs with downstream model behavior; Track key retrieval metrics such as accuracy, latency, and fallback rate; Implement caching, prefetching, and deduplication strategies to optimize system responsiveness
Qualifications
Minimum
4+ years in data engineering, ML infrastructure, or information retrieval; Experience building and deploying RAG pipelines or semantic search systems; Strong ML and Python skills and familiarity with retrieval libraries (e.g., Haystack, LangChain, Elasticsearch, Milvus); Proficiency with embedding models, vector similarity search, and document indexing; Familiarity with cloud platforms and MLOps tooling (e.g., Airflow, dbt, Docker)
Preferred
Knowledge of graph databases (e.g., Neo4j, TigerGraph) or knowledge graph design; Experience optimizing retrieval for LLMs (e.g., OpenAI, Anthropic, Mistral); Background in IR/NLP, Search Engineering, or Cognitive Computing; Degree in Computer Science, Information Systems, or a related field