ML Ops / Model Governance Engineer

United Software Group Inc.; Canada; Hybrid; CAD 100,000 - 150,000; Full time

Job summary

A software development company is seeking an experienced ML Ops / Model Governance Engineer to manage the lifecycle of machine learning models. This includes ensuring models are compliant, observable, and production-ready while supporting decision-making systems. The ideal candidate will have extensive experience in MLOps and model governance, working closely with various teams to maintain regulatory standards. This position is based in Halifax, CA, with remote work options available.

Qualifications

• 8+ years of experience in MLOps, ML platform engineering, or model governance.
• 5+ years managing ML model lifecycle governance in production environments.
• 4+ years of experience with model versioning, CI/CD, and deployment pipelines.
• 3+ years of hands-on experience with Python.
• 3+ years of experience implementing monitoring and alerting for models.

Responsibilities

• Manage the end-to-end lifecycle of machine learning models.
• Ensure models are governed, compliant, observable, and production-ready.
• Work with teams to meet enterprise ML regulatory standards.

Skills

MLOps

Model governance

Python

CI/CD

Monitoring and alerting

Regulatory compliance

Education

Bachelor's or Master's degree in Computer Science, Data Science, Engineering

Tools

MLflow

SageMaker Model Registry

AWS

Azure

GCP

Docker

Kubernetes

Job description

ML Ops / Model Governance Engineer

Location: Halifax, CA (Remote)

Job Summary

We are seeking an experiencedML Ops / Model Governance Engineerto manage theend-to-end lifecycle of machine learning models, ensuring they aregoverned, compliant, observable, and production-ready.

This role is critical in supportingNext Best Action (NBA)decisioning systems by maintainingtrust, transparency, and regulatory complianceacross enterprise ML platforms. The engineer will work closely withApplied ML Engineers, compliance, risk, legal, and platform teamsto ensure ML operations meet strict enterprise and regulatory standards.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field (or equivalent experience)
• 8+ years of experience in MLOps, ML platform engineering, or model governance
• 5+ years managingML model lifecycle governancein production environments
• 4+ years of experience withmodel versioning, CI/CD, and deployment pipelines
• 3+ years of hands-on experience withPython
• 3+ years of experience implementingmonitoring and alertingfor model performance, drift, and data quality
• 5+ yearsof experience working withregulatory, audit, and compliance requirementsfor ML systems

Preferred Qualifications

• Experience inregulated industries(Healthcare, Life Sciences, Financial Services, Insurance)
• Familiarity withmodel registries and governance tools(MLflow, SageMaker Model Registry, or equivalent)
• Knowledge ofExplainable AI (XAI), bias detection, and fairness frameworks
• Experience withcloud platforms(AWS, Azure, or GCP)
• Familiarity withDocker and Kubernetes
• Exposure todata governance and data lineage frameworks