Senior Machine Learning Engineer

The Senior Machine Learning Engineer will support the AI CoE's Machine Learning and Data Science initiatives by building, deploying, and operationalizing end-to-end machine learning solutions in cloud environments. The role is highly technical and hands-on, with ownership spanning data preparation, feature engineering, model development, production deployment, monitoring, and lifecycle management.

Key Responsibilities:

• Build and operationalize end-to-end machine learning solutions for enterprise use cases.
• Develop scalable ML pipelines covering data preparation, feature engineering, model development, deployment, and monitoring.
• Deploy and manage machine learning models in cloud environments using Azure ML, Databricks, MLflow, MLOps, and CI/CD practices.
• Take machine learning models from prototype through production deployment and ongoing operational support.
• Implement model monitoring and lifecycle management practices for production ML solutions.
• Integrate machine learning solutions into enterprise applications and operational workflows.
• Collaborate with data scientists and engineers to accelerate enterprise AI delivery from proof of concept through production.

Required Skills & Qualifications:

• Strong hands-on experience building, deploying, and operationalizing machine learning solutions in cloud environments.
• Expert-level proficiency in Python and SQL.
• Deep practical experience with Azure ML, Databricks, MLflow, CI/CD pipelines, and MLOps.
• Proven ability to take ML models from prototype to production and support them throughout their lifecycle.
• Experience building scalable ML pipelines and production-grade ML services.
• Strong understanding of model deployment, monitoring, and production support.
• Ability to work independently while collaborating effectively with data scientists and engineers.

Preferred Qualifications:

• Experience with Generative AI and LLM applications.
• Familiarity with agentic AI frameworks.
• Experience with GenAIOps practices, including evaluation, monitoring, deployment, and lifecycle management of GenAI solutions.
• Broader experience across cloud-native ML platforms and enterprise AI implementations.

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