VP, AI & Data

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What the market looks like

We've tracked 300+ senior-level AI engineering postings across the US in the last six months, with concentrated hiring in California, New York, and Texas. Professional services, technology, and financial services firms are leading demand. Compensation for this cohort ranges from $200K to $460K base salary, with strong candidates typically bringing 10+ years in data and AI infrastructure, proven track records scaling teams and systems, and deep fluency in modern data stacks and ML platforms. The strongest hires combine technical depth with business acumen—they understand both the engineering constraints and the commercial value of AI initiatives.

Job responsibilities

• Own the vision and strategy for AI and data capabilities across the organization, including architecture, governance, and integration with existing systems.

• Lead and scale a high-performing AI and data engineering team, setting technical direction, mentoring senior engineers, and fostering a culture of ownership and innovation.

• Partner with business and product leadership to translate AI opportunities into roadmap priorities, align initiatives with organizational goals, and drive business impact.

• Build and ship production AI/ML systems and data pipelines, ensuring scalability, reliability, and operational excellence from inception through deployment and monitoring.

• Establish data governance frameworks, security practices, and compliance standards appropriate to the organization's risk profile and regulatory environment.

• Drive adoption of modern data platforms and ML infrastructure, evaluate build-vs-buy decisions, and manage vendor relationships and technical debt strategically.

• Communicate technical progress, challenges, and business outcomes to executive stakeholders and board level, translating complexity into actionable insights.

Candidate requirements

• 10+ years of hands-on experience in data engineering, machine learning engineering, or AI infrastructure, with proven ability to architect and ship systems at scale.

• 5+ years leading technical teams (engineers, data scientists, or ML practitioners) in a direct management or principal technical leadership capacity.

• Deep fluency in modern data stacks, ML platforms, cloud infrastructure (AWS, GCP, or Azure), and production-grade data pipelines and models.

• Track record translating business requirements into technical strategy and delivering measurable AI/ML outcomes—not just theoretical knowledge.

• Demonstrated ability to communicate with both technical teams and non-technical executives, bridging the gap between engineering rigor and business needs.

• Experience with AI governance, data security, privacy, or compliance in regulated industries is a strong plus.