Job description

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. Key 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. Required Qualifications 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. Preferred Qualifications 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). Recruitment Fraud It has come to our attention various individuals and/or organizations are contacting people falsely pretending to recruit on behalf of Oxy. Please be aware that these recruiting scams and communications do not originate nor are they associated with our recruitment process. All Oxy job postings and offers will require a completed application through our company website. Oxy does not charge a fee at any stage of the recruiting process. We will never: • Ask you to pay for applications, interviews, meetings, processing, training or for any other fees • Use recruiting or placement agencies that charge candidates an advance fee of any kind or • Request personal information such as passport and bank account details at an early stage of our recruitment process. We recommend against responding to unsolicited business propositions or offers from people you don t know. Do not disclose your personal or financial details. If you believe you have been the victim of a recruiting scam, please contact your local police department. All qualified applicants will receive consideration for employment without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law.

Requirements

Must-haves

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.
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).
Recruitment Fraud
Oxy does not charge a fee at any stage of the recruiting process. We will never:
Ask you to pay for applications, interviews, meetings, processing, training or for any other fees
Use recruiting or placement agencies that charge candidates an advance fee of any kind or
Request personal information such as passport and bank account details at an early stage of our recruitment process.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningawsmlci/cdpythondockerkubernetes

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Professional Development