Sr. Machine Learning Engineer

Insight Enterprises, Inc.

7d•CA$140,000 - CA$160,000•Remote

About The Position

As a Senior Machine Learning Engineer, you will lead the modeling work on enterprise engagements where ML models are often integrated directly into multi-agent systems. You will be responsible for 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). This role offers opportunities to build anomaly detection and regression models, develop end-to-end pipelines for data quality checks, feature engineering, model training, tuning, evaluation, and batch or online inference. You will also migrate legacy ML workloads onto GEAP, translate existing models from platforms like Dataiku or SageMaker into KFP-based pipeline templates on GEAP, and own drift detection and retraining processes. This includes implementing distribution shift detection, accuracy regression checks, defining retraining thresholds and cadences, setting up alerting, and ensuring retraining deploys through the standard CD pipeline with canary, approval gate, and automated rollback. A key aspect of the role is to ensure all models are registered in GEAP Model Registry with comprehensive model cards. The company emphasizes continuous opportunities for upskilling, promotions, and career elevation.

Requirements

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

Responsibilities

Lead the modeling work on enterprise engagements where ML models are often integrated directly into multi-agent systems.
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).
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.
Ensure every model is registered in GEAP Model Registry with model cards capturing ownership, lineage, evaluation results, and lifecycle state.