DontApply.Club
Login Register

Senior Machine Learning Engineer

Epsilon Solutions Canada — Canada · Posted ~2 hours ago

Senior Full-time Hybrid

Skills

Python SQL Azure ML Databricks MLflow MLOps CI/CD machine learning model deployment model monitoring machine learning lifecycle management feature engineering cloud environments

🔓 Log in to save this job, tailor your resume & track your apply process — 7 days free, no card needed.

Log in to add to target list

Summary ✨ AI‑Generated

Join an AI-focused engineering organization as a Senior Machine Learning Engineer. You will build, deploy, and operationalize machine learning solutions in the cloud, taking models from prototype to production while developing scalable pipelines, monitoring systems, and enterprise integrations. The position combines deep technical ownership with hands-on MLOps work.

Highlights

Hands-on senior role delivering end-to-end machine learning solutions in cloud environments, from data preparation and model development through production deployment, monitoring, and ongoing support. Strong focus on scalable pipelines, enterprise integration, and MLOps.

Description

Senior Machine Learning Engineer Full Time Toronto, ON (Hybrid) Skills and Responsibilities: 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.