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, IAM, basic networking
• Familiarity with:
• Feature stores
• API‑based inference patterns

Nice to Have

• Experience with fraud, trust scoring, or risk modeling
• Exposure to PII‑sensitive systems
• Experience migrating batch ML pipelines to real‑time scoring
• Knowledge of explainable ML techniques