AI ML Operations Engineer

Oxy•Houston, TX

3d•Onsite

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 looking for an experienced and innovative individual contributor to fill the position of AI Machine Learning Operations Engineer within our AI Center of Excellence group based in Houston, TX. The incumbent will research, build, and design artificial intelligence systems to automate predictive models, and design machine learning systems, models and schemes.

Requirements

Bachelor's degree in computer science, mathematics, physics, engineering or related field required
A minimum of 3 years of experience and deep knowledge in orchestration methods and tools to optimally use computational infrastructure, and machine learning methods and algorithms
Strong Python or R programming skills required
Knowledge of Linux environment required
Strong knowledge of Microsoft products (Excel, Project, PowerPoint, Word, Power BI).
Experience developing, architecting, and running ML or deep learning workload in the Cloud.
Ability to express the intuition behind basic ML algorithms
Experience performing basic hyper parameter optimization
Ability to follow model training, deployment, and operational best practices
Experience with Docker for containerization and Kubernetes for orchestration
Knowledge of Terraform and Ansible for automating infrastructure management
Knowledge of YAML for automating model and application code deployment
Understanding of data engineering principles and databases (relational, NoSQL)

Nice To Haves

Master's degree is highly preferred.
Equivalent combination of advanced education and relevant experience will be considered.
Certified in Amazon Web services (AWS) cloud provider is highly preferred.
Ideally AWS certified in Machine Learning Specialty

Responsibilities

Designs production-grade ML systems end-to-end (data → training → deployment → monitoring) with experience
Owns production systems (debugging, incident response, reliability, SLAs, cost awareness)
Strong data engineering fundamentals (pipeline reliability, data quality, versioning, and data)
Builds reusable platform patterns that reduce duplication and enable teams to ship faster and more safely
Navigates cloud architecture complexity (AWS/Databricks/IaC) with a bias toward simplicity and maintainability
Works effectively across teams (data science, engineering, platform), can lead training, can influence adoption
Understanding of emerging agentic and LLM assisted development patterns