Data Scientist with Gen AI & Utilities

Job Role: Senior Data Scientist (GenAI & Utilities)
Location: Corpus Christi, TX / Wayne, IN (Hybrid)
Duration: Longterm
Note: Please share profiles with Utility, Energy, Oil & Gas & Industrial domains only.
Job Description:
We are seeking a highly skilled and motivated Senior Data Scientist with a strong background in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. The ideal candidate has direct experience applying AI within the utility, energy, or industrial sector. You will lead the design, development, and deployment of intelligent systems that translate unstructured operational data (technical manuals, maintenance logs, field reports) into actionable insights.
Key Responsibilities
• Develop RAG Pipelines: Design and optimize Retrieval-Augmented Generation (RAG) models to enhance LLM-driven troubleshooting and field operations.
• AI Model Development: Fine-tune and evaluate LLMs (e.g., GPT-4, Llama 3) for task-specific reasoning and accuracy.
• Knowledge Integration: Build high-quality Python code to integrate vector databases (e.g., Pinecone, Milvus, Weaviate) to store and retrieve technical utility documents efficiently.
• Operational Intelligence: Apply LLM solutions to analyze unstructured utility data, such as asset management logs, SCADA alerts, and maintenance records.
• Deployment: Work with software engineers to deploy and scale models in cloud environments (Azure/AWS).
• Collaboration: Work cross-functionally with field operations, engineering teams, and stakeholders to understand data needs and deliver solutions.
Required Qualifications:
• Experience: 10+ years of professional experience in Data Science/Machine Learning, with at least 1 year focusing specifically on LLMs and Generative AI.
• Technical Skills: Advanced proficiency in Python (PyTorch/TensorFlow, LangChain, LlamaIndex) and SQL.
• Domain Knowledge: Proven experience in the utility, energy, oil & gas, or heavy industrial sectors.
• RAG Expertise: Hands-on experience with vector embeddings, semantic search, and prompt engineering.

Regards
Mark R 
Email: