AI Commercial & ML Ops Engineer

MGM Resorts International•Home Office - US, TX, NV

2d•$130,100 - $173,500•Onsite

About The Position

The SHOW comes alive at MGM Resorts International. Have you ever wondered what it would be like to work in a place full of excitement, diversity, and entertainment? Are you enthusiastic about being a team player in one of the most fascinating industries in the world? At MGM Resorts, we seek individuals like YOU to create unique and show-stopping experiences for our guests. We are seeking a senior-level Artificial Intelligence and Machine Learning Operations Engineer to design, implement, and optimize scalable machine learning and artificial intelligence deployment pipelines that power real-world business impact. In this role, you will partner closely with Data Science, Data Engineering, and Analytics teams to ensure models are production-ready, performant, secure, and scalable across cloud platforms. You will automate and operationalize the full machine learning lifecycle, from data ingestion through retraining, while establishing best practices, governance frameworks, and enterprise standards for Artificial Intelligence and Machine Learning Operations. This is a highly visible individual contributor role that combines hands-on engineering with strategic influence across various initiatives.

Requirements

5+ years of prior relevant experience in machine learning or artificial intelligence engineering, Data Science, or Analytics Engineering
Bachelor’s degree in Computer Science, Data Engineering, or related field required
Deep experience with machine learning and artificial intelligence pipeline development and full lifecycle automation
Demonstrated ability to provide technical leadership in Machine Learning Operations strategies and pipeline standardization
Proven impact on improving reliability, scalability, and efficiency of machine learning and artificial intelligence solutions
Strong proficiency in Python and PySpark
Experience designing and implementing CI/CD for machine learning workflows, including version control systems such as Git and DVC
Experience with monitoring, logging, drift detection, and automated retraining frameworks
Experience with cloud platforms such as Databricks, AWS, GCP, or Azure
Proficiency in containerization and orchestration including Docker and Kubernetes
Experience with orchestration and lifecycle management tools such as Airflow, Kubeflow, MLflow, or similar platforms
Experience guiding teams on artificial intelligence automation best practices

Nice To Haves

Experience supporting marketing, revenue, or operations analytics teams preferred
Familiarity with TensorFlow, PyTorch, Scikit-learn, or similar machine learning frameworks preferred

Responsibilities

Design, build, and operate end-to-end machine learning and artificial intelligence pipelines supporting batch, streaming, and real-time inference use cases
Automate the full machine learning lifecycle including ingestion, feature engineering, training, validation, deployment, monitoring, and retraining
Implement CI/CD pipelines for machine learning systems with automated testing, validation gates, and controlled model promotion
Develop orchestration workflows using tools such as Airflow, Kubeflow, and MLflow for experiment tracking and governance
Optimize artificial intelligence workloads for performance, scalability, and cost efficiency using distributed compute and cloud-native services
Establish monitoring and observability frameworks including performance metrics, data quality checks, drift detection, and bias monitoring
Design automated retraining strategies including trigger-based, schedule-based, and performance-based refresh cycles
Create repeatable prompting frameworks and artificial intelligence guardrails to support safe and effective AI-assisted development
Implement access controls, secrets management, compliance standards, and security best practices across machine learning and artificial intelligence platforms
Evaluate and operationalize emerging artificial intelligence technologies and vendor tools, identifying measurable business value
Mentor engineers and data scientists on Machine Learning Operations best practices and influence enterprise-wide architectural standards