Closed listing
Senior Machine Learning Engineer
- Toronto, ON, Canada
Full-time
This opening has closed.
The original listing is kept below for reference.
Job details
- Job type
- Full-time
- Closing date
- Experience
- 7+ years of experience in Machine Learning Engineering, Data Science, or related fields.
- Posting language
- English
- Posted
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Search current openingsOriginal job description
Archived listing. The details below describe a past opening.
Position: Senior Machine Learning Engineer
Location: Toronto, On (Hybrid)
Employment Type: Full-Time
Experience Required: 8 + Years
Position Overview:
We are seeking a highly skilled Senior Machine Learning Engineer to design, build, deploy, and support scalable machine learning solutions for enterprise AI initiatives.
Key Responsibilities:
- Develop and operationalize end-to-end ML solutions, from data preparation and feature engineering to model deployment and production support.
- Build, automate, and maintain ML pipelines using Azure ML, Databricks, MLflow, and MLOps best practices.
- Deploy, monitor, troubleshoot, and optimize machine learning models in cloud environments.
- Implement CI/CD pipelines for ML workflows and ensure reliable model lifecycle management.
- Collaborate with Data Scientists, Data Engineers, and business stakeholders to accelerate AI solution delivery.
- Support model monitoring, performance tracking, retraining, and governance activities.
- Contribute to scalable AI platform development and integration with enterprise applications.
Required Skills:
- 7+ years of experience in Machine Learning Engineering, Data Science, or related fields.
- Strong expertise in Python, SQL, Machine Learning, Azure ML, Databricks, MLflow, and MLOps.
- Hands-on experience with model deployment, monitoring, CI/CD pipelines, and cloud-native ML platforms.
- Proven track record of moving ML models from prototype to production.
- Experience building scalable data and ML services in enterprise environments.
- Strong problem-solving, collaboration, and communication skills.
- Experience with Generative AI, Large Language Models (LLMs), Agentic AI frameworks, and GenAIOps.
- Knowledge of AI evaluation, monitoring, governance, and lifecycle management for Gini applications.
More about this job
Responsibilities
Develop, deploy, and support end-to-end machine learning solutions, including data preparation, feature engineering, production deployment, monitoring, and optimization. Build and maintain ML pipelines and CI/CD workflows, support model lifecycle and governance activities, and collaborate with technical teams and business stakeholders on enterprise AI solutions.
Requirements
Requires 7 or more years of experience in machine learning engineering, data science, or a related field, with strong expertise in Python, SQL, Azure ML, Databricks, MLflow, and MLOps. Candidates should have experience taking models from prototype to production, deploying and monitoring models, building scalable enterprise ML services, and working with Generative AI, LLMs, Agentic AI frameworks, and GenAIOps.
Skills
- Python
- SQL
- Machine Learning
- Azure ML
- Databricks
- MLflow
- MLOps
- Model Deployment
- Model Monitoring
- CI/CD Pipelines
- Cloud-Native ML Platforms
- Generative AI
- Large Language Models
- Agentic AI Frameworks
- GenAIOps
- AI Governance
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Data & Analytics
- Engineering
Keywords
- Machine Learning Engineering
- Data Science
- Python
- SQL
- Azure ML
- Databricks
- MLflow
- MLOps
- Machine Learning Pipelines
- Feature Engineering
- Model Deployment
- Model Monitoring
- CI/CD
- Cloud-Native Platforms
- Enterprise AI
- Generative AI
- Large Language Models
- Agentic AI Frameworks
- GenAIOps
- AI Evaluation
- AI Governance
- Model Lifecycle Management
- Data Engineering
- Production Support
- Scalable ML Services