Director, AI Platform

What The Market Looks Like

We've tracked 91 senior-level postings in AI operations across the US in the last six months, with strongest hiring in California, New York, and Texas. Compensation for director-level roles in this function ranges from $200K to $360K annually. The organizations filling these roles are typically scaling their AI infrastructure and tooling—they need leaders who can bridge platform engineering discipline with AI-specific operational demands. The strongest candidates bring hands-on experience building or managing ML infrastructure, a track record of shipping platform capabilities that reduce friction for data science or AI engineering teams, and the communication skills to translate between technical and business stakeholders.

Responsibilities

• Own the design, build, and ongoing operation of AI/ML platform infrastructure—including model serving, experiment tracking, data pipelines, and compute resource management.
• Lead a team of engineers focused on AI operations, MLOps, or platform engineering; set technical direction and hiring strategy for the function.
• Partner with data science, AI engineering, and product teams to understand operational friction points and ship platform improvements that accelerate time-to-production.
• Drive adoption of platform standards, best practices, and tooling; establish SLOs and observability for AI workloads in production.
• Manage infrastructure budgets, licensing, and cloud spend; optimize for cost, reliability, and developer velocity.
• Build and maintain the vendor and tool roadmap—evaluate, integrate, and retire platforms as the organization's AI maturity evolves.
• Collaborate with security, compliance, and IT leadership to embed governance, data quality, and risk management into platform design.

Requirements

• 7+ years of experience in AI/ML operations, MLOps engineering, platform engineering, or closely related infrastructure discipline—with at least 3 years in a leadership or senior individual contributor role.
• Demonstrated success building or scaling ML infrastructure platforms, model serving systems, or experiment management tooling; hands-on depth in at least one major cloud platform (AWS, GCP, Azure).
• Experience leading and mentoring engineering teams; ability to hire, develop talent, and set technical vision for a growing function.
• Strong understanding of MLOps patterns, ML lifecycle management, and the operational challenges that slow data science and AI engineering productivity.
• Proven ability to communicate technical decisions to non-technical stakeholders and translate business requirements into platform roadmap priorities.
• Comfort with ambiguity in early-stage AI initiatives; track record of shipping iteratively and adjusting strategy based on feedback from users (internal or external).