Machine Learning Engineer, Sr. - WFH
About the Company
Insight is a Fortune 500 solutions integrator that helps organizations navigate complex technology decisions and accelerate digital transformation. With expertise in cloud, data, artificial intelligence, cybersecurity, and intelligent edge technologies, the company works with clients to develop technology-driven solutions. Insight has more than 14,000 teammates globally and reported $8.2 billion in revenue in 2025.
About the Role
Insight is seeking a Senior Machine Learning Engineer to lead machine learning development for enterprise client engagements. The role focuses on building, deploying, and maintaining production-ready ML models that may be integrated into multi-agent systems. Responsibilities span data exploration, feature engineering, model development, deployment, monitoring, and retraining throughout the machine learning lifecycle. You will work extensively with the Gemini Enterprise Agent Platform (GEAP), formerly known as Vertex AI, to develop scalable ML pipelines, migrate existing workloads, monitor model performance, and manage model registration. The ideal candidate has strong applied machine learning experience, deep knowledge of GEAP, and a track record of delivering reliable models that support real-world business applications. This is a fully remote position.
Key Responsibilities
Build anomaly detection and regression models for enterprise use cases. Develop end-to-end machine learning pipelines covering data quality checks, feature engineering, model training, tuning, evaluation, and batch or online inference. Migrate legacy machine learning workloads from platforms such as Dataiku or SageMaker to GEAP. Convert existing models into Kubeflow Pipelines (KFP)-based pipeline templates on GEAP. Implement input and output distribution shift detection and accuracy regression checks to monitor model performance. Define model-specific monitoring thresholds and retraining schedules. Configure alerts and decision logic to determine when models require retraining. Ensure retraining workflows use standard continuous deployment pipelines with canary releases, approval gates, and automated rollback. Register models in the GEAP Model Registry and maintain model cards documenting ownership, lineage, evaluation results, and lifecycle status.
Required Qualifications
At least 5 years of applied machine learning engineering experience, including a minimum of 2 years deploying and operating models in production at scale. Deep experience with GEAP, including Model Registry, Pipelines using KFP, Experiments, Feature Store, and Workbench. Ability to establish machine learning projects and infrastructure from the ground up. Strong experience developing regression and anomaly detection models using gradient boosting techniques such as XGBoost, LightGBM, and CatBoost. Knowledge of statistical anomaly detection methods, including interquartile range (IQR), isolation forests, and robust z-scores. Experience with at least one deep learning approach to anomaly detection, such as autoencoders or normalizing flows. Ability to evaluate and justify model architecture decisions using empirical results. Strong production Python skills, including type annotations, testing, and packaging. Proficiency with pandas, NumPy, scikit-learn, and the GEAP SDK. Familiarity with the Kubeflow Pipelines (KFP) domain-specific language for pipeline development.
Compensation and Benefits
Base salary typically ranges from CAD $140,000 to CAD $160,000, with additional bonus and benefits available. Compensation decisions depend on factors such as experience, location, and individual circumstances. Candidates outside the stated salary range may also be considered where appropriate. Insight emphasizes employee well-being, family support, community engagement, and career development opportunities. Work Arrangement Fully remote position. Equal Employment Opportunity Insight is an equal opportunity employer. All qualified applicants receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, or other characteristics protected by applicable law.