Qode
Ontario, ON
Posted yesterday
Job Details:
Full-time
Executive
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
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Frequently Asked Questions
This is a full-time position based in Toronto, Ontario, focusing on design, build, and productionization of ML pipelines within an AWS-native architecture.
You will design, build, and productionize ML pipelines for a Trust Scoring platform, focusing on replayability, determinism, explainability, and MLOps, including batch and near-real-time inference and model monitoring.
Must-have skills include Python, PySpark, MLflow, model versioning, drift detection and monitoring, feature engineering, batch and streaming concepts, and experience with AWS services like S3, Spark/EMR, IAM.
Responsibilities include productionizing PoC models into reproducible pipelines, deterministic preprocessing for train vs serve parity, and enabling controlled rollouts with versioned feature and model deployments.
Yes, the role involves generating explainability artifacts such as reason codes and score attribution to support the Trust Scoring platform.
The role requires implementing feature drift detection, model performance monitoring, SLA and freshness validation, and supporting replay/recovery with idempotent design patterns.
The role requires 3–5 years of hands-on experience as a Machine Learning Engineer with strong experience taking ML models from development to production.
