Senior Machine Learning Engineer (Toronto)

Senior Machine Learning Engineer (Toronto)

01 Aug
|
LanceSoft
|
Toronto

01 Aug

LanceSoft

Toronto

**8+ Years Experience Required | BFSI experience is preferred

Role: Senior Machine Learning Engineer

Location: Toronto, ON (Hybrid)

Hiring Mode: Contract

**8+ Years Experience Required | BFSI experience is preferred

Pay: CAD 65 - 70/Hr.

Role Description:

Senior Machine Learning Engineer responsible for independently delivering enterprise AI solutions while collaborating effectively with data scientists and engineers. The role focuses on accelerating AI initiatives from proof of concept through production deployment. 101233-1

**8+ Years Experience Required | BFSI experience is preferred

Essential Skills:

Experienced Senior Machine Learning Engineer with robust hands-on experience in building, deploying, and operationalizing machine learning solutions in cloud environments. The ideal candidate should have expert knowledge of SQL and Python, along with deep experience in Azure ML, Databricks, MLflow, CI/CD pipelines, MLOps, model deployment, monitoring, and lifecycle management.

This highly technical,



hands-on role involves delivering end-to-end machine learning solutions, including data preparation, feature engineering, model development, deployment, monitoring, and ongoing production support. The candidate must have 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.

**8+ Years Experience Required | BFSI experience is preferred

📌 Senior Machine Learning Engineer (Toronto)
🏢 LanceSoft
📍 Toronto

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