Sr. Machine Learning Engineer

As an Senior Machine Learning Engineer, you will lead the modeling work on enterprise engagements where ML models are often integrated directly into multi-agent systems. We will count on you to span data exploration, feature engineering, model development across multiple business segments, integration into agent response loops, and operating the full MLOps lifecycle on Gemini Enterprise Agent Platform (GEAP). Along the way, you will get to:

• Build anomaly detection and regression models. Develop end-to-end pipelines that check data quality, engineer features, train, tune and evaluate models, and perform either batch or online inference.
• Migrate legacy ML workloads onto GEAP (formerly known as Vertex AI). Translate existing models from platforms such as Dataiku or SageMaker into KFP-based pipeline templates on GEAP.
• Own drift detection and retraining. Implement input and output distribution shift detection and accuracy regression checks. Define per-model thresholds and retraining cadence, set up alerting and logic for decisions on when to re-train a model. Ensure retraining deploys through the standard CD pipeline with canary, approval gate, and automated rollback.
• Register everything. Ensure every model is registered in GEAP Model Registry with model cards capturing ownership, lineage, evaluation results, and lifecycle state.
• Be AmbITious: This opportunity is not just about what you do today but also about where you can go tomorrow. When you bring your hunger, heart, and harmony to Insight, your potential will be met with continuous opportunities to upskill, earn promotions, and elevate your career.

• 5+ years applied ML engineering, with at least 2 years productionizing models at scale. You have shipped models that real users or downstream systems depend on, not just notebooks.
• Deep GEAP experience. Model Registry, Pipelines (KFP), Experiments, Feature Store, and Workbench. You can stand a project up from zero, not just consume an existing one.
• Strong regression and anomaly detection chops. Gradient boosting (XGBoost / LightGBM / CatBoost), classical statistical anomaly methods (IQR, isolation forests, robust z-scores), and at least one deep approach (autoencoders, normalizing flows). You can defend an architecture choice with empirical results, not preferences.
• Production Python. Type-annotated, tested, packaged. Comfortable with pandas, NumPy, scikit-learn, and the GEAP SDK. Familiar with KFP DSL for pipeline authoring.

The base salary range for this position is typically $140,000 CAD to $160,000 CAD with additional bonus and benefits available. However, compensation decisions are dependent on the facts and circumstances of each case, including experience and location, and we will also consider candidates outside of this range as necessary.

Insight at a Glance

• 14,000+ engaged teammates globally
• $8.2 billion in revenue in 2025
• Certified as a Great Place to work in 9 Countries in 2025
• Fortune 500 Company (No. 447) in 2025
• Received 25+ industry and partner awards in the past year
• $1.4M+ total charitable contributions in 2024 by Insight globally

Now is the time to bring your expertise to Insight. We are not just a tech company; we are a people-first company. We believe that by unlocking the power of people and technology, we can accelerate transformation and achieve extraordinary results. As a Fortune 500 Solutions Integrator with deep expertise in cloud, data, AI, cybersecurity, and intelligent edge, we guide organizations through complex digital decisions., At Insight, we celebrate diversity of skills and experience so even if you don’t feel like your skills are a perfect match - we still want to hear from you!