Sr. Technical AI Enablement Engineer

Responsibilities

• Implementation Planning: Lead customers in planning the implementation of ModelOp 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 ModelOp 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 ModelOp, 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 ModelOp 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 ModelOp 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 ModelOp, 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 ModelOp 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.

Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, or a related field.
• 10+ years of experience in technology-based consulting, technical enablement, or related roles. Experience in custom software delivery and complex system troubleshooting.
• Exceptional 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 workstreams.
• Experience with AI tools, platforms, and model development processes, along with a solid understanding of how governance and compliance mechanisms apply at each stage of the AI model lifecycle.
• Familiarity with AI regulations/guidelines (e.g. EU AI Act, OSFI E-23, NIST AI-RMF), ideally in specific verticals (e.g. SEC/OCC, FDA).
• Strong experience in working with technical teams to embed governance processes into an existing model lifecycle.

Preferred Skills

• Minimum of 1–3 years of hands-on experience in AI governance implementation, AI policy development, or data privacy/compliance in an AI context.
• Strong proficiency in Python for scripting, automation, and solution development.
• Experience working with AI/ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Familiarity with containerization and orchestration tools like Docker and Kubernetes.
• Knowledge of software deployment tools and CI/CD pipelines, such as Jenkins, GitHub, or BitBucket.
• Knowledge of cloud platforms (AWS, Azure, or GCP) and their governance tools.
• Leadership experience, with the ability to mentor junior team members and drive cross-functional initiatives.

Benefits

• ModelOp offers full benefits, including a retirement package and comprehensive health insurance.

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