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EPRI
Charlotte, North Carolina, United States
(on-site)
Posted
2 days ago
EPRI
Charlotte, North Carolina, United States
(on-site)
Job Function
Other
Lead Search and AI Engineer
The insights provided are generated by AI and may contain inaccuracies. Please independently verify any critical information before relying on it.
Lead Search and AI Engineer
The insights provided are generated by AI and may contain inaccuracies. Please independently verify any critical information before relying on it.
Description
Position Title: Lead Search and AI Engineer
Requisition ID: REQ-3852
Position Type: Full time
About Us:
About Us
Together Shaping the Future of Energy
EPRI provides thought leadership, industry expertise, and collaborative value to help the electricity sector identify issues, technology gaps, and broader needs that can be addressed through effective research and development programs for the benefit of society.
If you need help during the application process, please contact us at applyhelp@epri.com.
Read MoreJob Title:
Lead Search and AI EngineerLocation:
Charlotte, NC, North Carolina Home OfficeJob 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.
Key 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 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.
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 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.
PI281369677
Job ID: 82059434
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