Senior Machine Learning / MLOps Engineer

We are looking for a Senior Machine Learning / MLOps Engineer to join an enterprise AI Center of Excellence (AI CoE) and help build, deploy, and operationalize production-grade machine learning solutions.

This is a hands-on engineering role for someone who can take ML solutions from prototype to production, build scalable pipelines, deploy and monitor models in cloud environments, and support the complete ML lifecycle.

You will work closely with data scientists and engineering teams to turn AI/ML initiatives into reliable, scalable enterprise solutions.

What You'll Do
Design, build, deploy, and operationalize end-to-end machine learning solutions.
Develop scalable ML pipelines covering data preparation, feature engineering, model development, deployment, and monitoring.
Build and maintain production ML workflows using Azure ML, Databricks, MLflow, MLOps, and CI/CD.
Take machine learning models from proof of concept through production deployment.
Implement model monitoring, lifecycle management, and operational processes for production ML systems.
Integrate ML solutions with enterprise applications and operational workflows.
Troubleshoot and provide ongoing production support for ML pipelines and deployed models.
Partner with data scientists and software/data engineers to accelerate enterprise AI delivery.
Contribute to engineering standards and best practices for scalable and maintainable ML solutions.

What We're Looking For
5+ years of hands-on experience in machine learning engineering, ML platform engineering, MLOps, or a closely related field.
Strong hands-on experience building and deploying production machine learning solutions.
Expert-level proficiency in Python and SQL.
Deep experience with Azure ML, Databricks, and MLflow.
Strong understanding of MLOps and CI/CD pipelines.
Practical experience with model deployment, monitoring, and lifecycle management.
Proven experience taking ML models from prototype to production.
Experience developing scalable ML pipelines and production-grade ML services.
Experience working with cloud-based ML platforms and enterprise data/AI environments.
Ability to work independently and collaborate effectively with data scientists and engineers.

Nice to Have
Experience with Generative AI and LLM applications.
Experience with agentic AI frameworks.
Knowledge of GenAIOps, including evaluation, monitoring, deployment, and lifecycle management of GenAI solutions.
Experience supporting enterprise AI/ML platforms in production.