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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