Chief AI Officer

About the role

Axial Search is a specialist executive search firm built for one kind of hire: leaders who help organizations navigate AI transformation.

What The Market Looks Like

Chief AI Officer roles in MLOps remain rare at the executive level—we've tracked just 1 executive-level posting in this specific function over the last six months. Demand concentrates in Technology, Financial Services, Manufacturing, and Healthcare, with significant clustering in California, New York, and Texas. Compensation for this cohort ranges from $200K to $330K annually, reflecting the strategic weight of the role and scarcity of qualified candidates. The strongest leaders in this space bring deep hands-on MLOps expertise alongside executive experience scaling AI infrastructure, plus the ability to translate technical complexity into board-level business impact.

Job Responsibilities

• Own the end-to-end AI and MLOps strategy, setting technical vision and roadmap aligned with business objectives
• Build and lead a high-performing MLOps and AI engineering team, including hiring, mentorship, and organizational design
• Drive architecture and infrastructure decisions for ML model deployment, monitoring, and governance at scale
• Partner with executive leadership (CEO, CFO, COO) to define AI transformation priorities, budget allocation, and risk mitigation
• Establish governance, security, and compliance frameworks for AI systems across the organization
• Lead cross-functional collaboration with product, data science, and engineering teams to integrate AI capabilities into core products and operations
• Identify and execute high-impact AI use cases that drive revenue, efficiency, or competitive advantage
• Stay current on AI/ML trends and emerging technologies, evaluate tooling and vendor partnerships, and guide technology adoption decisions

Candidate Requirements

• 10+ years in machine learning engineering, MLOps, or AI infrastructure, with at least 5 years in leadership (director level or above)
• Deep technical foundation: hands-on experience designing and scaling ML systems, deployment pipelines, model monitoring, and production infrastructure
• Proven track record leading engineering teams of 10+ people and defining technical strategy at the organizational level
• Executive presence and comfort communicating with C-suite and board audiences; ability to translate technical work into business value
• Experience navigating AI governance, compliance, and risk frameworks in regulated or large enterprise environments
• Strong judgment on vendor selection, technology tooling decisions, and build-vs-buy trade-offs