Machine Learning Engineer at Insight Global

Ontario, Ontario, Canada -

Full Time

Start Date

Immediate

Expiry Date

29 Dec, 26

Salary

45000.0

Posted On

30 Sep, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

Industry

Consumer Services

Description

Required qualifications8+ 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 haveDirect 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.

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Responsibilities

Required qualifications8+ 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 haveDirect 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.