Associate AI / ML Engineer (Oil, Gas & Energy Domain)

Capgemini; Houston (TX); On-site; USD 120,000 - 150,000; Full time

Job summary

Capgemini is seeking an Associate AI / ML Engineer to join our Energy & Utilities practice in Houston. You will apply data-driven algorithms to upstream, midstream, or downstream oil and gas operations, collaborating with geoscientists, petroleum engineers, and software architects.

You will build ML models, develop scalable data pipelines from SCADA and sensor data, and deploy solutions in cloud environments with cross-functional teams.

Qualifications

• 2–4 years real-time ML model development in production.
• Legal internships are not counted.
• Experience in energy/oil & gas domain preferred.
• Strong Python and SQL for data processing.

Responsibilities

• Design, train, and evaluate ML models for energy workflows.

Skills

Python

SQL

Pandas

NumPy

Scikit-learn

PyTorch

TensorFlow

Docker

Git

RESTful APIs

Cloud platforms

Education

Bachelor’s or Master’s degree in Computer Science or Data Science

Electrical/Petroleum Engineering

Physics

Applied Mathematics

Quantitative field

Tools

Docker

Git

RESTful APIs

Databricks

Azure Machine Learning

AWS SageMaker

GCP

Job description

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.

• Locations:Capgemini -5051 Westheimer Rd #1800, Houston, TX 77056
• Company: Capgemini
• Experience Required: 2 to 4 Years of Real-Time Experience (Strictly No Internships)
• Domain Focus: Oil, Gas & Energy Industry

Position Summary

Capgemini is seeking an Associate AI / ML Engineer with a solid foundation in Data Science, Machine Learning, and Software Engineering to join our Energy & Utilities practice. In this role, you will apply data-driven algorithms and AI models to solve operational challenges across upstream, midstream, or downstream oil and gas operations.

You will collaborate with cross-functional technical teams and energy domain experts—including geoscientists, petroleum engineers, and software architects—to analyze complex sensor, drilling, and production datasets, build predictive models, and deploy scalable AI solutions into enterprise production workflows.

Key Responsibilities

• Model Development & Experimentation: Design, build, evaluate, and fine-tune supervised, unsupervised, and predictive machine learning models tailored to energy domain workflows (e.g., predictive maintenance, yield optimization, time-series forecasting).
• Data Pipeline Construction: Extract, clean, aggregate, and engineer features from massive industrial datasets, including SCADA systems, IoT sensors, well-logs, time-series operational data, and geospatial files.
• Deployment & Operations (MLOps): Assist in deploying, containerizing, and monitoring ML models in cloud environment pipelines (Azure, AWS, or GCP) to ensure model accuracy, stability, and latency requirements in production.
• Domain Collaboration: Translate complex energy sector problems (e.g., asset integrity, anomaly detection, seismic or reservoir data interpretation) into actionable machine learning solutions.
• Cross-Functional Teamwork: Partner with Capgemini senior data scientists, enterprise architects, and client leaders to deliver robust, production-ready digital solutions.

Required Qualifications & Experience

• Experience: Minimum 2 to 4 years of full-time, real-time working experience developing and deploying machine learning models in live production environments. (Note: Internship experience will NOT be counted toward this requirement).
• Industry Background: Direct hands-on experience or client project background within the Oil & Gas, Energy, Utilities, or Process Manufacturing sector.
• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Electrical/Petroleum Engineering, Physics, Applied Mathematics, or a related quantitative field.
• Core Programming: Strong proficiency in Python (Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow) and SQL for processing large-scale operational data.
• Domain Data Familiarity: Hands-on experience working with time-series data, sensor/IoT streams, SCADA signals, or spatial/geospatial datasets commonly found in oil and gas operations.
• Software Engineering: Familiarity with Git version control, RESTful APIs, Docker containerization, and writing clean, maintainable, modular code.

Preferred & Nice-to-Have Skills

• MLOps & Cloud Platforms: Experience with MLOps practices, CI/CD pipelines, and cloud platform tools (Azure Machine Learning, AWS SageMaker, Databricks).
• Generative AI & LLMs: Exposure to Large Language Models (LLMs), Generative AI integration, or AI Agent architectures applied to enterprise/technical documentation.
• Domain Workflows: Understanding of specific energy workflows such as well production forecasting, drilling optimization, asset health, or digital twin technologies.