We are looking for an experienced Senior Machine Learning Engineer to support the AI CoE’s Machine Learning and Data Science initiatives. • The ideal candidate will have strong hands-on experience building, deploying, and operationalizing machine learning solutions in cloud environments, with expert knowledge of SQL and Python and deep experience with Azure ML, Databricks, MLflow, CI/CD pipelines, MLOps, model deployment, monitoring, and lifecycle management. • This is a highly technical, hands-on role focused on delivering end-to-end ML solutions, including data preparation, feature engineering, model development, deployment, monitoring, and ongoing production support. • The role requires proven experience taking models from prototype to production, building scalable ML pipelines and services, and integrating machine learning solutions into enterprise applications and operational workflows. • Experience with cloud-native ML platforms, automated deployment processes, and production support is essential. • Experience with Generative AI, LLM applications, agentic AI frameworks, and GenAIOps practices — including evaluation, monitoring, deployment, and lifecycle management of GenAI solutions — would be considered an asset. • In addition to technical depth, we are looking for someone who can work independently, collaborate effectively with data scientists and engineers, and help accelerate the delivery of enterprise AI solutions from proof of concept through production deployment. 

We are looking for an experienced Senior Machine Learning Engineer to support the AI CoE’s Machine Learning and Data Science initiatives. • The ideal candidate will have strong hands-on experience building, deploying, and operationalizing machine learning solutions in cloud environments, with expert knowledge of SQL and Python and deep experience with Azure ML, Databricks, MLflow, CI/CD pipelines, MLOps, model deployment, monitoring, and lifecycle management. • This is a highly technical, hands-on role focused on delivering end-to-end ML solutions, including data preparation, feature engineering, model development, deployment, monitoring, and ongoing production support. • The role requires proven experience taking models from prototype to production, building scalable ML pipelines and services, and integrating machine learning solutions into enterprise applications and operational workflows. • Experience with cloud-native ML platforms, automated deployment processes, and production support is essential. • Experience with Generative AI, LLM applications, agentic AI frameworks, and GenAIOps practices — including evaluation, monitoring, deployment, and lifecycle management of GenAI solutions — would be considered an asset. • In addition to technical depth, we are looking for someone who can work independently, collaborate effectively with data scientists and engineers, and help accelerate the delivery of enterprise AI solutions from proof of concept through production deployment.