Senior ML Engineer at Qode

, Ontario, Canada -

Full Time

Start Date

Immediate

Expiry Date

31 Aug, 26

Salary

0.0

Posted On

02 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, PySpark, MLflow, AWS, MLOps, Feature Engineering, Model Monitoring, CI/CD, Batch Inference, Real-time Scoring, Drift Detection, S3, Spark/EMR, Explainable ML, Model Versioning, Data Quality Validation

Industry

Software Development

Description

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

Responsibilities

Design and productionize ML pipelines for a Trust Scoring platform focusing on replayability, determinism, and explainability. Implement MLOps foundations including MLflow, CI/CD pipelines, and model monitoring within an AWS-native architecture.