Posted August 22, 2026
ML OPS AI ENGINEER II
Robert Half
Coppell, TX, US
Full Time
57.47USD - 66.54USD per hour
Job Description
Job Description
We are looking for an ML OPS AI Engineer II to support the delivery of machine learning solutions from development through live production in Coppell, Texas. This Long-term Contract opportunity is ideal for a hands-on engineer who can strengthen ML infrastructure, improve deployment reliability, and partner closely with AI teams to operationalize models at scale. The role focuses on building repeatable systems, increasing observability, and ensuring model workflows remain efficient, stable, and cost-conscious across cloud-based environments.
Responsibilities:
• Lead the end-to-end operationalization of machine learning models, moving solutions from experimentation into dependable production environments.
• Develop and support ML infrastructure, automated pipelines, and deployment frameworks that improve reliability and reduce manual effort.
• Create and manage containerized workloads using Docker and coordinate production services through Kubernetes.
• Establish and maintain CI/CD processes for model training, packaging, testing, and release management.
• Implement tools and standards for experiment tracking, feature lineage, and model version control to enable reproducibility.
• Build monitoring solutions that surface system health, model behavior, and data drift, helping teams respond quickly to production issues.
• Provision and optimize cloud and compute resources to support both training and inference workloads effectively.
• Improve scalability, operational visibility, and cost efficiency across deployed AI services.
• Partner with data scientists and ML engineers to simplify deployment pathways and align platform capabilities with model development needs.• 3 to 5 years of experience in MLOps, machine learning platform engineering, or AI infrastructure roles.
• Demonstrated success deploying, maintaining, and improving machine learning models in production settings.
• Hands-on experience with Azure and managing compute resources for model training and inference.
• Strong background in containerization and orchestration technologies, including Docker and Kubernetes.
• Practical experience building CI/CD pipelines for machine learning or broader software delivery workflows.
• Proficiency in Python, along with experience in automation and infrastructure-as-code practices.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical discipline.
• Working knowledge of AI/ML concepts, with the ability to collaborate effectively in a fast-paced, agile environment.
Responsibilities:
• Lead the end-to-end operationalization of machine learning models, moving solutions from experimentation into dependable production environments.
• Develop and support ML infrastructure, automated pipelines, and deployment frameworks that improve reliability and reduce manual effort.
• Create and manage containerized workloads using Docker and coordinate production services through Kubernetes.
• Establish and maintain CI/CD processes for model training, packaging, testing, and release management.
• Implement tools and standards for experiment tracking, feature lineage, and model version control to enable reproducibility.
• Build monitoring solutions that surface system health, model behavior, and data drift, helping teams respond quickly to production issues.
• Provision and optimize cloud and compute resources to support both training and inference workloads effectively.
• Improve scalability, operational visibility, and cost efficiency across deployed AI services.
• Partner with data scientists and ML engineers to simplify deployment pathways and align platform capabilities with model development needs.• 3 to 5 years of experience in MLOps, machine learning platform engineering, or AI infrastructure roles.
• Demonstrated success deploying, maintaining, and improving machine learning models in production settings.
• Hands-on experience with Azure and managing compute resources for model training and inference.
• Strong background in containerization and orchestration technologies, including Docker and Kubernetes.
• Practical experience building CI/CD pipelines for machine learning or broader software delivery workflows.
• Proficiency in Python, along with experience in automation and infrastructure-as-code practices.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical discipline.
• Working knowledge of AI/ML concepts, with the ability to collaborate effectively in a fast-paced, agile environment.
