Lead Search and AI Engineer

EPRI • Charlotte, NC

1d • $145,000 - $175,000

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.
• Specialized Expertise 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.
• Prompt Engineering 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

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

Benefits

• This role is eligible to participate in EPRI’s annual incentive program.
• The amount of incentive varies and is subject to the terms and conditions of the plan.
• This role is eligible to participate in EPRI’s standard employee benefit programs, which currently include the following: medical, dental, vision, 401k, STD/LTD and paid family leave, life and accident insurance, paid time off (flexible vacation, sick leave, and holiday pay).

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 1,001-5,000 employees