Azure MLOps Engineer

Job Description We are seeking a Senior MLOps Engineer to lead the architecture, deployment, and operationalization of machine learning solutions in Azure-based production environments. This role is designed for a highly experienced engineer who can own end-to-end MLOps architecture, contribute immediately, and partner closely with data science, platform, and engineering teams. This position bridges model development and enterprise-scale deployment, with a strong emphasis on Azure infrastructure, MLOps best practices, and solution design. Key Responsibilities u2003u2022 Architect, deploy, and manage end-to-end MLOps solutions in Azure from development through production. u2003u2022 Design and maintain Azure-native ML infrastructure, including Azure ML, Azure DevOps, AKS, and related services. u2003u2022 Build and manage CI/CD pipelines for model training, validation, deployment, and monitoring. u2003u2022 Partner with data scientists to transform research models into scalable, production-ready services. u2003u2022 Develop and manage APIs and services to expose ML models across the organization. u2003u2022 Implement monitoring, retraining, versioning, and governance strategies for ML models. u2003u2022 Optimize compute (CPU/GPU), cost, and performance within Azure environments. u2003u2022 Ensure Linux-based systems and containers are optimized for ML workloads. u2003u2022 Provide architectural guidance and contribute quickly with minimal ramp-up. Based on knowledge and years of experience, this position offers an hourly pay rate of $65-80/hr We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: Skills and Requirements u2022 Senior-level experience in MLOps, ML engineering, or DevOps with ML systems. u2003u2022 Strong experience architecting full ML solutions, not just deploying individual models. u2003u2022 Deep Azure expertise, including: u2003u2003u25cb Azure Machine Learning u2003u2003u25cb Azure DevOps (CI/CD) u2003u2003u25cb Azure Kubernetes Service (AKS) u2003u2003u25cb Azure storage and networking concepts u2003u2022 Strong Python and SQL skills. u2003u2022 Experience with ML frameworks (PyTorch, TensorFlow, Scikit-learn). u2003u2022 Proficiency with Docker, Kubernetes, and CI/CD pipelines. u2003u2022 Strong understanding of MLOps lifecycle, governance, and production best practices. u2003u2022 Ability to operate independently and contribute immediately in a senior role. u2022 Azure certifications (Azure ML, Azure AI Engineer, or Azure Solutions Architect). u2003u2022 Experience optimizing ML workloads using GPUs/CPUs in Azure. u2003u2022 Strong understanding of ML theory, algorithms, and data science concepts. u2003u2022 Experience supporting multiple ML systems in production environments.

Created: 2026-04-04