MLOps Specialist

Domino's•Ann Arbor, MI

About The Position

Domino’s is seeking a Technology Ops Support Specialist to join our Machine Learning Operations (MLOps) team. This role focuses on the hands-on implementation, operation, and support of ML/AI solutions built on platforms such as Azure and Databricks. You’ll work closely with senior engineers and data scientists to deploy models, maintain data pipelines, monitor system performance, and troubleshoot production issues. The position emphasizes day-to-day execution, including model deployment support, environment configuration, automation, and integration with downstream systems. You’ll collaborate with teams across Data & AI, Decision Science, Engineering, Infrastructure, and Marketing to ensure ML solutions run reliably in production. You’ll also partner with the GenAI Enablement Lead to help implement custom models, connectors, and tooling that support broader enterprise AI adoption.

Requirements

Bachelor’s in Computer Science, Software Engineering, Mathematics, or related field (Master’s preferred)
2-3+ years in technical project or delivery management with leadership experience
Strong knowledge of machine learning methods, especially for personalization and digital innovation
Hands-on expertise in Python, SQL, and functional programming. Experience with PySpark is a plus
Experience with containerization technologies like Docker and Kubernetes is preferred but not required
Experience supporting production applications or ML systems in an operation or platform support role
Hands-on experience with CI/CD pipelines and deployment automation
Experience supporting model hosting or API-based services
Ability to troubleshoot issues across environments and escalate appropriately
Working knowledge of Azure, Databricks, and GitHub from an operational or support perspective
Experience using MLflow for experiment tracking and/or model registry support
Experience supporting GitHub Actions and GitHub Actions runners
Experience supporting or integrating services with Azure API Management (APIM)
Exposure to GenAI systems, agent-based workloads, or LLM-backed services
Experience supporting agent hosting or MCP-style services is preferred but not required
Ability to self-start and self-direct work in a dynamic, cross-functional environment
Excellent communication skills with the ability to engage, influence, and encourage partners and team members to drive collaboration and alignment
Experience creating engaging and compelling stories and presentations from data and effectively communicating analysis results with a diverse non-technical audience

Nice To Haves

Master’s preferred
Experience with containerization technologies like Docker and Kubernetes is preferred but not required
Experience supporting agent hosting or MCP-style services is preferred but not required

Responsibilities

Operate and support production and non-production ML/GenAI services, including model, agent, and MCP server hosting, ensuring availability and basic scalability
Perform day-to-day‑operational tasks such as deployments, restarts, configuration updates, and environmental support
Troubleshoot platform issues and work with senior engineers or platform teams to resolve defects
Support and maintain CI/CD pipelines for ML and GenAI workloads
Build, update, and troubleshoot GitHub Actions workflows used for model, agent, and application deployment
Support GitHub Actions runners (hosted or self-hosted), including operational troubleshooting and reliability issues
Support teams using MLflow or Azure Machine Learning for experiment tracking and model registration
Assist with model promotion workflows across environments (dev → test → prod)
Help internal teams follow established MLOps standards and deployment practices
Provide guidance on “how to ship” within the existing platform, not on model design
Support onboarding and operation of ML and GenAI endpoints exposed through Azure API Management (APIM)
Assist with API configuration, versioning support, and operational troubleshooting
Work with application teams to ensure services are integrated cleanly with platform APIs
Monitor deployed ML and GenAI services for availability and basic performance issues
Respond to incidents, alerts, and operational issues following documented procedures
Contribute to operational documentation, runbooks, and support playbooks