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.