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
