Advisor IT Systems

Oxy • Houston, TX

6d

About The Position

Oxy produces, markets and transports oil and natural gas to maximize value and provide resources fundamental to life. The company leverages its global leadership in carbon management to advance lower-carbon technologies and products. Headquartered in Houston, Oxy primarily operates in the United States, Middle East and North Africa. To learn more, visit Oxy Oxy strives to attract and retain talented employees by investing in their professional development and providing rewarding opportunities for personal growth. Our goal is to meet the highest employer standards by ensuring the health and safety of our employees, protecting the environment and positively impacting our communities where we do business. We are seeking a mid‑career MLOps / AI Ops Engineer to support the deployment, monitoring, and lifecycle management of machine learning and advanced analytics solutions across upstream Oil & Gas operations. This role bridges data science, cloud engineering, and operations to ensure reliable, scalable, and secure AI systems in production.

Requirements

• 5+ years of experience in data engineering, software engineering, MLOps, or AI Ops.
• Good grasp of software architecture principles and systems design
• Strong proficiency in Python for production‑grade ML workflows.
• Hands‑on experience with AWS (e.g., S3, EC2, EKS/ECS, SageMaker, Lambda, CloudWatch).
• Experience deploying and supporting ML models in production environments.
• Familiarity with CI/CD tools, Docker, and Kubernetes.
• Understanding of ML lifecycle management, model monitoring, and data drift.

Nice To Haves

• Experience supporting analytics or ML solutions in upstream Oil & Gas or energy.
• Knowledge of time‑series, forecasting, or physics‑informed ML workloads.
• Experience with infrastructure‑as‑code (Terraform, CloudFormation).

Responsibilities

• Design, build, and maintain MLOps pipelines and platforms for model training, deployment, monitoring, and retraining using AWS.
• Operationalize ML models for upstream use cases (e.g., production optimization, subsurface modeling, drilling analytics).
• Implement CI/CD, model versioning, experiment tracking, and performance monitoring.
• Collaborate with data scientists, data engineers, and domain experts to move models from development to production.
• Ensure reliability, observability, governance, and compliance of ML systems.
• Troubleshoot production issues related to data, models, and infrastructure.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 5,001-10,000 employees