About the role  

We are hiring a senior hands-on MLOps Engineer who blends strong DevOps fundamentals with machine learning delivery. You will build and run the CI/CD pipelines that move ML and application artifacts safely across environments on Azure, and keep those services reliable in production.  

Key responsibilities 

• Design, build and maintain CI/CD pipelines for ML models, data components and supporting services 

• Create pipelines that promote artifacts (models, packages, containers, configuration) across environments, such as dev, test, staging and production, with the right gates and approvals 

• Integrate pipelines with Azure services, for example Azure DevOps or GitHub Actions, Azure Machine Learning, container registries, Key Vault and storage 

• Write and maintain Python scripts and tooling that automate pipeline steps, deployments and operational tasks 

• Implement and manage authentication and authorization for pipelines, including service principals, managed identities, secrets handling and least-privilege access 

• Run and support services in production: monitoring, alerting, incident response, troubleshooting and continuous improvement 

• Work with data scientists, ML engineers and platform teams to turn experimental work into repeatable, production-ready delivery 

• Document pipelines and operating procedures so other teams can use and support them 

Required qualifications 

• Proven experience with CI/CD processes and tooling in a production setting 

• Experience designing pipelines that promote artifacts across multiple environments 

• Hands-on experience integrating with Azure services 

• Strong Python skills, including scripting and automation 

• Experience building authentication mechanisms for pipelines (service principals, managed identities, token-based access, secrets management) 

• Experience running services in production and supporting them operationally 

• Working knowledge of MLOps concepts: model packaging, versioning, deployment and monitoring 

• Solid understanding of version control, infrastructure as code and containerization 

• Clear communication and the ability to collaborate with data science and engineering teams 

Nice to have 

• Hands-on experience with GitHub Copilot in day-to-day engineering work 

• Exposure to AI adoption strategies in an enterprise environment, such as rollout, enablement, governance and measuring impact 

• Experience with Azure Machine Learning or comparable MLOps platforms 

• Familiarity with monitoring and observability for ML workloads 

Why join us  

People are at the Heart of our Business. By investing in people, we achieve exceptional results for our clients and create new opportunities for our teams to thrive. Check out this page for more details. 

If you have any questions contact our Talent Acquisition team on ta.admin@acuityanalytics.

For more details about Acuity Analytics please see here: Read here.