Sr. Model Governance Engineer
Work full remote (in the US)Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSPythonSQLGCPJavaAzureRESTful APIsJSON
Requirements
- Bachelor’s degree, or higher, in a technical or quantitative field (Computer Science, Engineering, Statistics, Mathematics, or similar)
- 5+ years of experience in model governance, model validation, analytics, or related engineering roles
- Experience managing model documentation, approvals, controls, or lifecycle tracking
- Understanding of AI/ML models and how they are developed, tested, and used
- Familiarity with model validation concepts and benchmarking approaches
- Working knowledge of programming languages such as Java and Python
- Familiarity with common data formats such as CSV and JSON
- Experience writing or reviewing SQL and querying databases, including working with datasets for testing or validation
- Understanding of application development concepts and software development lifecycles
- Familiarity with SDKs, APIs, and integration patterns used in production systems
- Experience working with or supporting systems deployed on cloud platforms (e.g., AWS, Azure, or GCP)
- Ability to review technical implementations to understand how models are built, deployed, and monitored
- Experience using documentation or tracking tools to support governance, review, or audit activities
- Awareness of data privacy and security considerations when working with model data and datasets
Responsibilities
- Maintain the enterprise model inventory, including model owners, purpose, risk rating, and deployment status
- Apply model risk ratings and confirm required governance steps are completed
- Coordinate model release approvals, evidence collection, and sign off processes
- Manage versioning, archiving, and traceability for models and related documentation
- Ensure models have complete and standardized documentation, such as Model Cards
- Document model intent, assumptions, limitations, and known risks
- Capture explainability considerations and any human in the loop processes used
- Maintain data lineage and dataset information for training, validation, and monitoring
- Ensure required validation evidence is available, including performance and testing results
- Support stress testing or review activities for higher risk models
- Help set up and support ongoing model monitoring activities
- Trigger governance reviews when model, data, or scope changes occur
- Partner with engineering, data science, risk, compliance, and governance teams
- Produce clear and consistent governance documentation for both technical and non-technical audiences
- Support the preparation and review of datasets used for model testing and validation
- Support or participate in governance reviews, validations, or review meetings
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