Summary
✨ AI‑Generated
A machine learning engineering role focused on designing, deploying, and monitoring production-scale ML solutions. The position requires strong software fundamentals, cloud ML experience, data expertise, and experience improving complex systems.
Highlights
Offers a senior-level machine learning engineering opportunity involving production AI systems, cloud ML platforms, data optimization, and advanced engineering practices.
Description
Pay: 70-85
Contract Duration: 12 MO
Work Arrangement: Hybrid Toronto
We may use artificial intelligence tools to assist with the screening, assessment, or selection of potential applicants for this position.
Vacancy: This posting is for a currently vacant role, and the successful candidate will be hired into an existing open position.
Required qualifications
8+ years in ML engineering, data engineering, or software engineering, with substantial recent time at a staff/lead level of technical scope.Strong Python and software engineering fundamentals — testing, packaging, code review, refactoring legacy or exploratory code without breaking behavior.Production experience with Azure ML: jobs, pipelines, compute, model registry, endpoints, MLflow tracking.Strong SQL and Snowflake experience, including performance and cost tuning on large tables.CI/CD experience with Azure DevOps (or equivalent) for ML workloads.Demonstrated experience building ML monitoring and observability in production — not just standing up a dashboard, but defining what to measure and what to do when it moves.Working knowledge of explainability methods (e.g.
SHAP, permutation importance, forecast decomposition) and the judgment to know their limits.Time series forecasting experience: hierarchical forecasts, intermittent demand, proper backtesting and evaluation design.Clear written and verbal communication with non-technical stakeholders.
Nice to have
Direct experience with o9 Solutions, or comparable planning platforms (SAP IBP, Kinaxis, Blue Yonder).CPG, retail, or consumer goods demand planning and supply chain context.Databricks/Spark, dbt, Azure Data Factory, Power BI.Feature store, containerization, or infrastructure-as-code experience.
