Retrieval Augmented Generation Rag Jobs in Texas

Senior Data Scientist – GenAI / RAG

Location: Houston, TX
Employment Type: Full-Time
Experience: 7–15 Years
Salary: $125,000 – $150,000 per year
Work Authorization: ,

Client: Tavant Technologies
Industry: Information Technology / Enterprise Products

Job Summary

Tavant Technologies is seeking a Senior Data Scientist – GenAI / RAG to join its Enterprise Products team. The ideal candidate will have a strong foundation in traditional Data Science and Machine Learning, combined with hands-on experience developing Generative AI, Large Language Model (LLM), Retrieval-Augmented Generation (RAG), and Agentic AI solutions.

The candidate should be experienced in applying advanced analytical and machine learning techniques to complex business problems, working with large datasets, and translating data-driven insights into scalable enterprise solutions.

Experience in the Energy, Utilities, Oil & Gas, Renewable Energy, or Natural Resources domain is highly preferred.

The successful candidate should also be comfortable collaborating with product, engineering, and business teams and communicating technical concepts effectively to both technical and non-technical stakeholders.

Key Responsibilities

• Develop, train, evaluate, and deploy machine learning and predictive models to solve complex business problems.
• Apply statistical analysis and advanced data science techniques to generate actionable business insights.
• Design and implement Generative AI and LLM-based solutions for enterprise applications.
• Develop and enhance RAG pipelines for enterprise knowledge retrieval and question-answering use cases.
• Contribute to Agentic AI workflows and intelligent enterprise solutions where applicable.
• Analyze large and complex datasets to identify trends, patterns, opportunities, and business risks.
• Collaborate with Product Managers, Software Engineers, Data Engineers, and business stakeholders to integrate AI/ML solutions into enterprise products.
• Develop scalable data science solutions using modern cloud and big-data technologies.
• Evaluate model performance and continuously improve accuracy, reliability, and scalability.
• Communicate analytical findings, model results, and recommendations clearly to technical and non-technical stakeholders.
• Support production deployment, monitoring, troubleshooting, and optimization of ML and GenAI solutions.
• Stay current with emerging developments in Machine Learning, Generative AI, LLMs, RAG, and data science technologies.
• Provide technical guidance and mentorship to junior data scientists when required.

Required Qualifications

• 7+ years of professional experience in Data Science / Machine Learning.
• Strong programming experience with Python or R.
• Strong understanding of Machine Learning, statistical modeling, and predictive analytics.
• Hands-on experience with machine learning frameworks such as:
  • Scikit-learn
  • XGBoost
  • CatBoost
  • TensorFlow
  • PyTorch
• Hands-on experience with Generative AI and Large Language Models (LLMs).
• Strong practical experience developing RAG / Retrieval-Augmented Generation solutions.
• Experience with LLM evaluation, LLMOps, or MLOps is highly desirable.
• Experience with big-data technologies such as Databricks, Snowflake, Spark, or PySpark.
• Strong SQL and database experience.
• Experience working with large-scale datasets and data pipelines.
• Experience with at least one major cloud platform such as AWS, Azure, or Google Cloud.
• Experience with data visualization tools such as Power BI, Tableau, or similar platforms.
• Strong analytical, problem-solving, and critical-thinking skills.
• Excellent written and verbal communication skills.

Preferred Qualifications

• Experience in the Energy / Utilities / Oil & Gas / Renewable Energy / Natural Resources industry.
• Experience supporting enterprise products or large-scale enterprise applications.
• Experience with Agentic AI / AI Agents and frameworks such as LangChain or LangGraph.
• Experience with vector databases and semantic search.
• Experience with ML model deployment, monitoring, and lifecycle management.
• Master''s or Ph.D. in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative discipline.
• Experience working directly with customers or business stakeholders.

Preferred Industry Background

Candidates with experience supporting organizations in the following areas are highly preferred:

Utilities / Grid

• Duke Energy
• NextEra Energy
• Southern Company
• Exelon
• National Grid
• PG&E

Oil & Gas / Natural Resources

• Schlumberger / SLB
• Halliburton
• Chevron
• ConocoPhillips

Energy Technology

• Hanwha Qcells

Consulting – Energy Practices

• Accenture
• Deloitte
• Capgemini

Core Technical Skills

Data Science:
Python, R, SQL, Statistical Modeling, Predictive Analytics, Machine Learning

Machine Learning:
Scikit-learn, XGBoost, CatBoost, TensorFlow, PyTorch

Generative AI:
GenAI, LLMs, RAG, Retrieval-Augmented Generation, Agentic AI

Big Data:
Databricks, Snowflake, Apache Spark, PySpark

MLOps / LLMOps:
MLflow, Model Evaluation, Model Monitoring, Model Deployment

Cloud:
AWS, Azure, Google Cloud

Visualization:
Power BI, Tableau

Ideal Candidate Profile

The ideal candidate is not purely a GenAI/LLM engineer or an academic Data Scientist. We are looking for someone who combines:

Traditional Data Science + Machine Learning + GenAI/LLM + RAG + Enterprise Product Experience

Candidates with direct Energy-domain experience and the ability to communicate effectively with customers and business stakeholders will receive strong preference.

Please submit candidates with recent, hands-on experience in Data Science, Machine Learning, and GenAI/RAG.