Sr. Technical AI Enablement Engineer

Model Op is seeking a highly skilled Sr Technical AI Enablement Engineer to join our Customer Success team specializing in AI governance. This role will be responsible for driving implementation and consumption of the Model Op software, by helping our customers translate their AI governance strategy into a practical, scalable AI Governance 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 as well as within 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.

• Implementation Planning:
  Lead customers in planning the implementation of Model Op within the customer’s existing AI program. This involves analyzing their existing policies and requirements, and crafting the right implementation roadmap and architecture across software deployment, capability enablement, integrations, training, and AI use case onboarding. This involves working with the customer to design how to integrate Model Op into the customer’s broader technology ecosystem.
• Technical Troubleshooting:
  When customer issues are raised, take lead on triaging the issue, and work with the customer 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.
• 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.
• Customer Enablement:
  Work closely with the customer to drive consumption of Model Op software, by onboarding new AI use cases in the platform. This specifically will involve numerous presentations to the customer’s business, governance, and technology teams to showcase the capabilities and benefits of using Model Op, as well as to help these teams onboard their specific use cases into the platform.
  
  To be successful, the enablement engineer must assist the customer in driving the AI governance program, occasionally serving as a change agent to adopt best practices within customer teams.
• 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…