VP, AI Engineering

Job Responsibilities

• Own the technical strategy and roadmap for AI/ML initiatives, setting priorities that align with business outcomes and company architecture constraints.
• Build and lead a high-performing AI/ML engineering team, including hiring, mentoring, and creating a culture of rigor around model quality, data pipelines, and production reliability.
• Possess strong foundational understanding of machine learning workflows—data pipelines, feature engineering, model training, evaluation, and deployment—and how engineering decisions impact model performance.
• Ship production AI/ML systems end-to-end—from problem definition and data strategy through deployment, monitoring, and continuous improvement.
• Evaluate and integrate emerging ML tools, frameworks, and infrastructure—making deliberate choices on build versus buy for core platform components.
• Establish engineering standards, MLOps practices, and governance frameworks that ensure models stay performant, auditable, and maintainable over time.
• Communicate technical progress and blockers to non-technical stakeholders, securing resources and alignment on trade-offs between speed and robustness.

Candidate Requirements

• 10+ years of software engineering experience, with at least 5 years building, shipping, or leading AI/ML systems in production environments.
• Demonstrated experience managing and growing engineering teams of 3+ people, with a track record of hiring talent and developing them into strong individual contributors or leaders.
• Strong foundational understanding of machine learning workflows—data pipelines, feature engineering, model training, evaluation, and deployment—and how engineering decisions impact model performance.
• Hands-on technical fluency across the ML stack: you've written code for model training, worked with common frameworks (PyTorch, TensorFlow, scikit-learn), and debugged data and model quality issues.
• Experience setting technical strategy, making architectural trade-offs, and translating business needs into engineering roadmaps at a team or organizational level.
• Comfort communicating with both technical and non-technical audiences—explaining what's possible, what's risky, and what's worth building.