SeniorMLEngineer

**Job Title** : Senior ML Engineer **Location** : Toronto, CA **Duration** : Full-time Role Summary We are looking for a Senior ML Engineer to design, build, and productionize ML pipelines for a Trust Scoring platform, with a strong focus on replayability, determinism, explainability, and MLOps best practices. This role is hands‑on and platform‑focused, working across batch inference, real‑time scoring, feature engineering, and model monitoring, within an AWS‑native architecture. Key Responsibilities ML Engineering & Model Productionization * Productionize PoC ML models into reproducible, governed pipelines * Implement deterministic preprocessing for train vs serve parity * Develop batch and near‑real‑time inference workflows * Generate explainability artifacts (reason codes, score attribution) MLOps Foundations * Implement and maintain: * MLflow (experiments, model registry) * CI/CD pipelines for ML * Champion/Challenger model frameworks * Enable: * Controlled rollouts (shadow, advisory, active scoring) * Versioned feature and model deployments Feature & Data Engineering Collaboration * Design and consume features from: * Batch and low‑latency feature stores * Canonical entity models (subscriber, device, SIM) * Collaborate on: * Data quality validation * Schema contracts * Drift detection (feature + score) Monitoring & Platform Reliability * Implement: * Feature drift detection * Model performance monitoring * SLA and freshness validation * Support replay and recovery using idempotent design patterns Required Skills & Experience Core Experience * 3–5 years hands‑on experience as a Machine Learning Engineer * Strong experience taking ML models from development to production Technical Skills (Must‑Have) * Programming: Python, PySpark * ML/MLOps: * MLflow * Model versioning and promotion * Drift detection and monitoring * Data: * Feature engineering * Batch and streaming concepts * Large‑scale datasets Cloud & Platform * AWS experience (preferred): * S3, Spark/EMR, IA