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ML Ops
2 weeks ago
ML Ops / Model Governance Engineer
Location:
Halifax, CA (
Remote
)
Job Summary
We are seeking an experienced
ML Ops / Model Governance Engineer
to manage the
end-to-end lifecycle of machine learning models
, ensuring they are
governed, compliant, observable, and production-ready
.
This role is critical in supporting
Next Best Action (NBA)
decisioning systems by maintaining
trust, transparency, and regulatory compliance
across enterprise ML platforms. The engineer will work closely with
Applied ML Engineers, compliance, risk, legal, and platform teams
to 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
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 model performance, drift, and data quality - 5+ years
of experience working with
regulatory, audit, and compliance requirements
for ML systems
Preferred Qualifications
- Experience in
regulated industries
(Healthcare, Life Sciences, Financial Services, Insurance) - Familiarity with
model registries and governance tools
(MLflow, SageMaker Model Registry, or equivalent) - Knowledge of
Explainable AI (XAI)
, bias detection, and fairness frameworks - Experience with
cloud platforms
(AWS, Azure, or GCP) - Familiarity with
Docker and Kubernetes - Exposure to
data governance and data lineage frameworks
