AI Enablement Engineer

Model Op is seeking a highly skilled AI Enablement Engineer to join our Customer Success team specializing in AI model governance and lifecycle management. This role will be responsible for driving implementation and consumption of the Model Op platform by helping our customers translate their AI governance strategy into a practical and scalable implementation. This role involves working closely with business teams, governance and risk stakeholders, and technical teams to enable them to use Model Op to accelerate deployment and enforce AI governance policy for all AI use cases across the enterprise.

The enablement engineer will guide customers through planning, implementation, and enablement of the Model Op software, ensuring adherence to the customer’s policy, regulatory requirements, and best practices. This position will be highly visible within the Model Op organization and with our customers, offering a unique opportunity to shine as an industry expert for Artificial Intelligence.

Utah, Chicago or Remote

Full-time

Model Op is the leading AI Lifecycle Automation and Governance Software that brings AI initiatives to market faster, at scale, while mitigating risks and instilling trust across the entire enterprise. Model Op gives executives and AI leaders comprehensive visibility into all their AI initiatives – including GenAI, in-house, third-party vendor, and embedded systems – ensuring AI is profitable, compliant, and risk-managed.

Through automation and integrations, Model Op empowers enterprises to quickly address the critical governance and scale challenges necessary to protect and fully unlock the transformational value of enterprise AI – resulting in effective and responsible AI systems.

• Process Design
  :
  Collaborate with customer teams to translate the customer’s AI governance policies and procedures into actionable and automated workflows within Model Op, whilst simultaneously advising on best practices from other customers and experience.
• Inventory & Reporting Configuration
  : lead customers in designing the appropriate customizations for the metadata structure, use case forms, and dashboards within Model Op to align with their inventory and reporting requirements.
• Testing & Monitoring Enablement
  : lead customer sessions in identifying and configuring the right set of tests and monitors for their specific AI use cases / models. Assist in troubleshooting related data or other issues that arise during the customer’s usage of the testing and monitoring capabilities.
• Technical Troubleshooting
  :
  When customer issues are raised, take lead on triaging the issue, and work with the customer’s technical team to obtain information (logs, etc.), conduct initial troubleshooting, and identify the next course of action. Work internally to assign the appropriate resource to address the issue, and, once solved, to help the customer test and implement the provided solution to address the issue.
• Training & Support
  :
  Lead customer workshops, training sessions, and knowledge transfer activities to ensure customer teams understand and can effectively use Model Op for their daily AI governance work.
• Documentation
  :
  Document processes, configurations, and troubleshooting steps for both technical and non-technical audiences. Contribute to internal and external knowledge bases.
• Learning
  :
  Stay updated on the latest developments in AI governance and related technologies to guide customers and inform product enhancements.

• Bachelor’s degree in Computer Science, Software Engineering, or a related field.
• 4+ years of experience in technology-based consulting, technical enablement, or related roles. Experience in custom software delivery and complex system troubleshooting.
• Strong proficiency in Python for scripting, automation, and solution development.
• Strong presentation skills for all audiences, from senior business executives to data scientists, architects, and governance teams.
• Proven ability to translate loose AI governance requirements into actionable implementation details across multiple work streams.

• Minimum of 1–3 years of hands‑on experience in AI governance…