Azure AI and Intelligent Search Engineer

Description Role Summary We're looking for an engineer to design and build the semantic and vector search layer that powers natural-language, source-grounded retrieval across a large body of government records. This role owns the retrieval pipeline end to end — from indexing through relevance tuning to answer grounding — and is central to making AI-assisted search trustworthy and auditable. Key Responsibilities Design and implement hybrid retrieval pipelines combining semantic (vector) and keyword search using Azure AI Search. Build and tune embedding, chunking, and indexing strategies for large, heterogeneous document sets. Integrate Azure OpenAI to power retrieval-augmented generation (RAG) with source-grounded, citation-backed responses. Tune search relevance and evaluate retrieval quality against defined accuracy benchmarks. Collaborate with the Document Intelligence and SharePoint teams to ensure indexed content stays synchronized with source systems and metadata. Document architecture decisions and retrieval evaluation results for government stakeholders and auditors. Requirements Required Qualifications Production experience with Azure AI Search (or Cognitive Search), including semantic ranker and vector/hybrid search. Hands-on experience with embeddings, RAG architectures, and retrieval pipelines. Experience with Azure OpenAI or comparable LLM platforms in a production setting. Demonstrated work on chunking, indexing, and search relevance tuning. Experience building source attribution / citation-backed responses (prompt grounding). Strong SQL and API integration skills. Preferred Qualifications Experience with retrieval evaluation frameworks (e.g., RAGAS or equivalent). Familiarity with AI guardrails, PII redaction, and prompt-injection defenses. Prior work on federal, government, or other highly regulated implementations. Experience with multi-agent orchestration (e.g., LangGraph) is a plus, not required.