Senior ML Engineer - Remote, Enterprise ML & MLOps Lead

Insight; Sacramento (CA); À distance; USD 99 000 - 113 000; Plein temps

Résumé du poste

Insight is seeking a Senior Machine Learning Engineer to drive modeling on enterprise engagements with ML models integrated into multi-agent systems. You will own data exploration, feature engineering and full MLOps lifecycle on GEAP, guiding deployment and governance across projects.

You will build robust regression and anomaly detection models, migrate workloads to GEAP, and establish drift detection and retraining strategies with automated rollback.

Qualifications

• 5+ years applied ML engineering experience.
• Productionizing models at scale (2+ years).
• Deep GEAP experience: Model Registry, Pipelines (KFP), Experiments, Feature Store, Workbench.
• Strong regression and anomaly detection capabilities.
• Production Python: type-annotated, tested, packaged; familiar with pandas/NumPy/scikit-learn.

Responsabilités

• Lead modeling work on enterprise engagements with ML models integrated into multi-agent systems.
• Span data exploration, feature engineering, model development across multiple business segments, and MLOps lifecycle on GEAP.
• Build anomaly detection and regression models; develop end-to-end pipelines with data quality checks.
• Migrate legacy ML workloads onto GEAP; translate models into KFP-based templates.
• Own drift detection, retraining cadence, and alerting for retraining decisions.
• Ensure retraining deploys through CD pipeline with canary and automated rollback.
• Register models in GEAP Model Registry with ownership, lineage and evaluation results.

Description du poste

Insight is seeking a Senior Machine Learning Engineer to drive modeling on enterprise engagements with ML models integrated into multi-agent systems. You will own data exploration, feature engineering and full MLOps lifecycle on GEAP, guiding deployment and governance across projects.

You will build robust regression and anomaly detection models, migrate workloads to GEAP, and establish drift detection and retraining strategies with automated rollback.