
Senior Machine Learning Engineer
- Toronto, ON
- $100,300-125,400 per year
- Permanent
- Full-time
As a Senior Machine Learning Engineer at Kraft Heinz, you will be part of the DIPP – (Decision Intelligence Products & Platforms) ML Engineering team. This person will be Operating horizontally across core functions such as Supply Chain, Manufacturing, Commercial, R&D, HR, and Marketing, this team drives value through scalable ML systems and robust MLOps practices. You will lead and develop the deployment, monitoring, and governance of machine learning models, ensuring high-performance, production-grade AI systems across the enterprise.What’s on the menu?MLOps & Model Lifecycle Automation
- Lead MLOps practices for end-to-end model management, versioning, testing, deployment, and monitoring, ensuring traceability and reproducibility
- Ensure models meet enterprise SLAs for reliability, security, and performance
- Contribute to the design and evolution of the internal ML platform with reusable components, documentation, and engineering standards
- Build shared infrastructure that supports experimentation, model reproducibility, auditability, and scale across domain.
- Develop, deploy and operationalize predictive models that improve operational efficiency, forecast demand, and manage business risks
- Work closely with data scientists and SMEs to translate prototypes into production-grade ML systems
- Bachelor’s or Master’s in Computer Science, Engineering, or a related field
- Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
- Proficiency in Python, R, or other programming languages commonly used in machine learning
- 3+ years of experience in ML engineering and MLOps with a focus on production-scale systems
- Strong knowledge of MLOps tools (e.g., AzureML, MLflow, Kubeflow, Airflow), Model Observability (e.g. Aporia) and cloud platforms (AWS, Azure, GCP)
- Deep understanding of model governance, drift detection, and Responsible AI standards
- Proficiency in CI/CD for ML using Azure DevOps practices
- Experience with Snowflake or similar platforms for data pipeline integration
- Ability to build and maintain strong relationships with stakeholders, ensuring effective communication, collaboration, and alignment on shared goals