Find Jobs
Find Jobs Near You – Available Work in Your Location
ML OPS AI ENGINEER II
Career Insights for Machine Learning Engineer
See where this job fits in the broader career landscape. Knowing your career path helps you see what's possible from here.
Scorecard
Based on Texas data
Review key factors to help you decide if this role fits your goals. How is this calculated?
What they do
A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
$124,597 / year median in Texas
Job Description
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.