MLOps Governance Engineer

405 W. Greenlawn Ave Lansing, Michigan 48910

• MLOps Governance Engineer: AI Governance Role Description & Roadmap

Best Backgrounds

The MLOps Governance Engineer operates at the intersection of ML platform engineering and AI compliance infrastructure. This role builds the technical systems that make AI governance enforceable at scale:
automated bias detection pipelines, immutable audit trails, model documentation systems, and deployment gates that prevent non-compliant models from reaching production. Glassdoor reports MLOps Engineers averaging $161,317 nationally (63 salaries), with Senior MLOps Engineers at $203,298
.

Define AI roles and training. The AI Roles, Responsibilities & Training Policy: clarify who owns what for AI.

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This is not yet a standardized title
. Job listings use “Senior MLOps Engineer (Governance),” “ML Platform Governance Engineer,” and “MLOps/LLMOps Engineer — Governance & Compliance.” The closest explicit match is Empower’s Director of Software Engineering — MLOps, ML Governance listing, requiring 10+ years overseeing ML governance frameworks including model documentation, validation, explainability, and fairness/bias monitoring.

Hiring industries: technology (
Amazon/AWS, Google, Microsoft, NVIDIA
), financial services (
JPMorgan Chase, Capital One, Empower
), healthcare (
CVS Health, United Health Group
), government/defense (
General Dynamics, Leidos, MITRE
), and consulting (
Deloitte, Accenture
). Financial services demand is driven by SR 11-7 model risk management requirements.

Also Known As Senior MLOps Engineer (Governance) ML Platform Governance Engineer MLOps/LLMOps Engineer — Governance ML Infrastructure Engineer AI Platform Engineer ML Operations Lead Director of MLOps & ML Governance

9.8x growth in MLOps job postings over five years (Linked In Talent Insights / People In AI). $179,600 median base for MLOps Engineers broadly (Linked In). Workers with AI skills earn a 56% wage premium over peers without them (PwC AI Jobs Barometer).

Knowledge Insight — EU AI Act Technical Requirements

Why this role is becoming mandatory: EU AI Act Article 9 requires providers of high-risk AI systems to implement risk management systems with continuous monitoring
. Article 17 mandates quality management systems including governance procedures. These requirements translate directly into MLOps Governance Engineer responsibilities: automated monitoring, audit trail generation, documentation systems, and deployment gates. Organizations cannot fulfill these obligations through manual processes at scale — making the technical governance infrastructure this role builds a regulatory necessity
. (Source: EU AI Act Articles 9, 17)

ML Pipeline Governance Checks

Design and maintain automated fairness tests, data quality gates, and model validation steps embedded in every deployment pipeline.

ML Pipeline Governance Checks Design and maintain automated fairness tests, data quality gates, and model validation steps embedded in every deployment pipeline. REALITY CHECK + Your governance checks are code, not paperwork. Automated bias detection runs on every model push, catching issues before they reach production.

Track data drift, concept drift, and performance degradation across production models using Evidently AI, Fiddler, or Why Labs.

Model Monitoring Dashboards Track data drift, concept drift, and performance degradation across production models using Evidently AI, Fiddler, or Why Labs. REALITY CHECK + When monitoring flags anomalies, you investigate root causes. Is it a data distribution shift, a labeling issue, or a genuine model failure?

Maintain model registries ensuring proper documentation and approval records before any model reaches production. Automate model card generation.

Model Registry & Documentation Maintain model registries ensuring proper documentation and approval records before any model reaches production. Automate model card generation. REALITY CHECK + Model cards per Mitchell et al. are your deliverable.…