Senior ML Engineer

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