AI Retrieval & Relevance Engineer

Job Description

A client of Insight Global is seeking an experienced AI Retrieval & Relevance Engineer to architect, implement, and optimize retrieval-augmented generation (RAG) and hybrid search systems that provide accurate, grounded context to LLMs and AI agents. This role owns the retrieval pipeline end-to-end to ensure their systems efficiently retrieve, contextualize, and support accurate outputs across business applications.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances.

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• Proven experience designing and tuning information retrieval systems, vector search, and RAG frameworks.
• Strong knowledge of vector and hybrid search technologies (e.g., FAISS, Weaviate, Elasticsearch, Milvus/Pinecone equivalents).
• Proficiency in Python and familiarity with ML tooling (PyTorch/Tensor Flow helpful, especially for rerankers).
• Familiarity with distributed processing/orchestration tools (e.g., Spark, Airflow, Kubeflow) as needed for indexing and eval pipelines. Experience with rerankers / learning-to-rank, query understanding, and relevance tuning.
• Experience with LLM fine-tuning, prompt engineering, and RAG optimization.
• Familiarity with agentic systems and multi-step retrieval (iterative retrieval, tool-use patterns).
• Cloud and scalable storage/indexing platform experience.