Lead AI Engineer

Electric Power Research Institute, Inc.; Tennessee; On-site; USD 145,000 - 175,000; Full time

Benefits offered by this job

Medical insurance

401k

Paid time off

Dental insurance

Vision insurance

Job summary

Electric Power Research Institute, Inc. is seeking a Lead Search and AI Engineer to design, implement, and operate AI-driven document processing and search systems using Azure AI Search, RAG, and Knowledge Graphs.

You will build scalable architectures and optimize embeddings for improved retrieval. The role requires strong Python skills, experience with AKS, and a background in AI pipelines, including prompt engineering and multi-system orchestration.

Qualifications

• 7+ years solving complex program and system problems in a business environment.
• 5+ years in AI/ML, cloud-based search, and document processing.
• Expertise in Query Orchestration for AI pipelines.
• Strong knowledge of RAG architectures for AI-powered search.
• Hands-on with Azure AI Search, Document Intelligence, and Cognitive Services.
• Proficient in vector search, embeddings, and hybrid retrieval.
• Experience with Kubernetes (AKS) and containerized deployments.
• Familiarity with Tesseract OCR, PyMuPDF, Pillow.
• Strong Python development skills for AI pipelines.
• Domain adaptation for EPRI’s technical language.

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.

Skills

Azure AI Search

RAG architectures

Knowledge Graphs

Query Orchestration

Prompt Engineering

Python development

Vector search

Kubernetes

Tesseract OCR

PyMuPDF

Education

Bachelor's or Master's in Computer Science

Professional certification

Tools

Azure OpenAI

LangChain

FAISS

Weaviate

Pinecone

Cognitive Services

Job description

Job Title

Lead Search and AI Engineer

Location

Tennessee Home Office

Job Summary and Description

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.

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.

Required Skills & Experience

• Bachelors or Masters Degree in Computer Science or related areas, applicable professional certification with 7+ years of progressive experience providing solutions in complex program/system problems in a business environment and 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 in 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.
• Understanding of export control compliance and secure document handling.

Preferred Qualifications

• 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 other vector databases such as FAISS, Weaviate, Pinecone.
• Background in NLP, document classification, and entity extraction.

Salary & Benefits

The salary range for this position is $145,000 USD to $175,000 USD annually. This salary range is an estimate, and the actual salary may vary based on various factors, including without limitation applicant's education, experience, skills, and abilities, as well as internal equity and alignment with market data. The salary may also be adjusted based on applicant's geographic location. This role is eligible to participate in EPRI’s annual incentive program. 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).

Equal Opportunity Employer

EPRI is an equal opportunity employer. EEO/AA/M/F/VETS/Disabled.