VP AI - Platform Engineering

kadence; New York (NY); Hybrid; USD 250,000 - 350,000; Full time

Job summary

Kadence seeks a Vice President of AI Platform Engineering to define and drive the enterprise AI platform strategy across training, deployment, and model serving. You will lead a global team of 35+ engineers, collaborating with security, governance, and product leadership to deliver secure, scalable AI foundations.

The role emphasizes MLOps, data pipelines, governance, and developer experience in a hybrid, multi-location environment. Strong executive communication is essential.

Qualifications

• Significant leadership experience within platform engineering, infrastructure, cloud, or enterprise technology organizations.
• Recent ownership of modern AI platform or AI infrastructure initiatives.
• Experience building or leading platforms used to train, deploy, serve, evaluate, and operate AI workloads at enterprise scale.
• Strong understanding of GPU infrastructure, distributed compute, scalable inference, AI data platforms, and high-throughput pipelines.
• Depth across MLOps, model serving, orchestration, evaluation, observability, governance, and model lifecycle management.
• Experience building internal developer platforms or self-service engineering capabilities.
• A track record of leading major technology transformations over 18–24-month horizons.
• Experience directing large, multidisciplinary organizations and influencing programs involving hundreds of contributors across matrixed teams.
• Demonstrated impact on engineering productivity, platform modernization, operational maturity, reliability, and organizational effectiveness.
• Executive-level communication skills and the ability to connect technical strategy with measurable business outcomes.
• Strong technical credibility, with the depth to challenge architecture decisions and guide principal, staff, and senior platform engineers.

Responsibilities

• Define and execute the enterprise AI platform and infrastructure strategy.
• Build scalable environments for AI training, inference, evaluation, and model serving.
• Lead the development of GPU and accelerator-based compute platforms.
• Establish AI data platforms, high-speed pipelines, and supporting data infrastructure.
• Scale MLOps, model lifecycle management, orchestration, observability, and evaluation capabilities.
• Create self-service AI developer platforms that improve engineering productivity and developer experience.
• Use AI and intelligent automation to improve operational efficiency, reliability, security, and platform performance.
• Establish enterprise standards for AI governance, security, auditability, and production readiness.
• Lead build-versus-buy decisions across AI infrastructure, developer tooling, model platforms, and orchestration frameworks.
• Partner with product engineering, information security, legal, privacy, procurement, and senior business leaders.
• Lead architecture reviews and set the engineering quality bar for AI systems entering production.
• Recruit, develop, and lead a multidisciplinary global engineering organization.

Skills

Leadership experience

AI platform ownership

GPU infrastructure

MLOps

Executive communication

Cross-functional collaboration

Job description

Overview

Location: Hybrid — New York, NY; Dallas, TX; or Minneapolis–St. Paul, MN

A global information and technology organization is making a significant investment in AI as a foundational enterprise capability.

We are seeking a Vice President of AI Platform Engineering to lead the strategy, development, and operation of the platforms and infrastructure that enable AI adoption across the enterprise.

This is not an applied AI product role. The mandate is to build and scale the foundational capabilities that allow engineering and product teams to develop, train, deploy, and operate AI workloads securely and reliably.

The successful candidate will lead a global organization of 35+ engineers and serve as a senior technical and organizational leader across platform engineering, infrastructure, developer experience, security, governance, and enterprise AI transformation.

What You’ll Own

• Define and execute the enterprise AI platform and infrastructure strategy.
• Build scalable environments for AI training, inference, evaluation, and model serving.
• Lead the development of GPU and accelerator-based compute platforms.
• Establish AI data platforms, high-speed pipelines, and supporting data infrastructure.
• Scale MLOps, model lifecycle management, orchestration, observability, and evaluation capabilities.
• Create self-service AI developer platforms that improve engineering productivity and developer experience.
• Use AI and intelligent automation to improve operational efficiency, reliability, security, and platform performance.
• Establish enterprise standards for AI governance, security, auditability, and production readiness.
• Lead build-versus-buy decisions across AI infrastructure, developer tooling, model platforms, and orchestration frameworks.
• Partner with product engineering, information security, legal, privacy, procurement, and senior business leaders.
• Lead architecture reviews and set the engineering quality bar for AI systems entering production.
• Recruit, develop, and lead a multidisciplinary global engineering organization.

What We’re Looking For

• Significant leadership experience within platform engineering, infrastructure, cloud, or enterprise technology organizations.
• Recent, substantive ownership of modern AI platform or AI infrastructure initiatives.
• Experience building or leading platforms used to train, deploy, serve, evaluate, and operate AI workloads at enterprise scale.
• Strong understanding of GPU infrastructure, distributed compute, scalable inference, AI data platforms, and high-throughput pipelines.
• Depth across MLOps, model serving, orchestration, evaluation, observability, governance, and model lifecycle management.
• Experience building internal developer platforms or self-service engineering capabilities.
• A track record of leading major technology transformations over 18–24-month horizons.
• Experience directing large, multidisciplinary organizations and influencing programs involving hundreds of contributors across matrixed teams.
• Demonstrated impact on engineering productivity, platform modernization, operational maturity, reliability, and organizational effectiveness.
• Executive-level communication skills and the ability to connect technical strategy with measurable business outcomes.
• Strong technical credibility, with the depth to challenge architecture decisions and guide principal, staff, and senior platform engineers.