Azure AI Search Engineer — RAG & Source-Grounded Retrieval

Xtreme Solutions Inc; Atlanta (GA); On-site; USD 120,000 - 170,000; Full time

Job summary

Xtreme Solutions Inc. in Atlanta seeks an engineer to design and build the semantic and vector search layer powering natural-language, source-grounded retrieval across government records. You will own the retrieval pipeline end-to-end.

You will collaborate with AI and data teams to ensure auditability, compliance, and robust attribution in responses, while optimizing relevance and performance.

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.

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.

Skills

Teamwork

Problem solving

Tools

Azure AI Search

Azure OpenAI

Embeddings

RAG architectures

Chunking

Indexing

SQL

API integration

Job description

Xtreme Solutions Inc. in Atlanta seeks an engineer to design and build the semantic and vector search layer powering natural-language, source-grounded retrieval across government records. You will own the retrieval pipeline end-to-end.

You will collaborate with AI and data teams to ensure auditability, compliance, and robust attribution in responses, while optimizing relevance and performance.