Lead Search and AI Engineer

EPRI•North Carolina Home Office, NC

110d•$145,000 - $175,000•Onsite

About The Position

The Lead AI Engineer is to design, implement, and support AI-driven document processing, retrieval, and search solutions using Azure AI Search, Retrieval-Augmented Generation (RAG), Knowledge Graphs, Query Orchestration, Prompt Engineering, and Kubernetes-based container deployments.

Requirements

Bachelors or Masters Degree in Computer Science or related areas, applicable professional certification with 7+ years of progressive experience providing solutions to complex program/system problems in a business environment in 5+ years in AI/ML, cloud-based search, and document processing.
Expertise in Query Orchestration for complex AI pipelines.
Strong knowledge of RAG architectures for AI-powered search.
Hands-on experience with Azure AI Search, Document Intelligence, and Cognitive Services.
Proficiency in vector search, embeddings, and hybrid retrieval techniques.
Experience with Kubernetes (AKS) and containerized deployments.
Familiarity with Tesseract OCR, PyMuPDF, and Pillow.
Strong Python development skills for AI pipelines.
Search & RAG Semantic, BM25, similar ranking and vector search optimization.
Custom scoring profiles and relevance tuning.
Evaluation metrics (nDCG, MRR, precision@k).
Query rewriting, synonym maps, and semantic expansion.
Integration with RAG and LLM pipelines for optimized context retrieval.
Systematic prompt design and evaluation.
RAG-oriented prompting with grounding and guardrails.
Instruction hierarchies and multi-agent orchestration.
Domain adaptation for EPRI’s technical language.
Continuous improvement through telemetry and quality analysis.

Nice To Haves

Experience with hybrid cloud AI solutions (on-prem + cloud).
Familiarity with Azure OpenAI, LangChain, or AI Foundry.
Deep knowledge of multi-index query orchestration.
Expertise in Azure AI search semantic and vector profiling.
Expertise in vector other databases such as (FAISS, Weaviate, Pinecone).
Background in NLP, document classification, and entity extraction.
Understanding of export control compliance and secure document handling.

Responsibilities

Build AI-driven search and document intelligence systems using Azure AI Search, Knowledge Graphs, and RAG techniques.
Develop strategies to route and structure user queries efficiently across multiple retrieval systems.
Implement and fine-tune applications for intelligent knowledge retrieval from structured and unstructured documents.
Deploy and manage AI applications using Azure Kubernetes Service (AKS) for scalability.
Enhance document retrieval through optimized embeddings and hybrid search techniques.
Utilize tools like Tesseract OCR, PyMuPDF, and Pillow for document processing.
Connect with Profile APIs, Product Metadata, and Downloads to enrich indexing and search capabilities.
Ensure adherence to export control restrictions and secure document handling best practices.
Troubleshoot and optimize AI-based workflows for performance and reliability.
Work closely with business and technical teams to refine AI-powered document solutions.