Head of AI Operations
United States · Remote · Permanent
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What the market looks like
We've tracked 12 senior-level AI operations postings in the US over the last six months, concentrated in California, New York, and Texas across technology, financial services, and professional services firms. Compensation for this cohort ranges from $200K to $350K annually. The strongest candidates bring hands-on experience scaling AI infrastructure and workflows, a track record of translating AI strategy into repeatable operational processes, and the ability to build cross-functional partnerships between engineering, product, and business teams. Organizations are increasingly hiring for this role to bridge the gap between AI development velocity and operational maturity.
Job responsibilities
Own the design and implementation of AI operations frameworks, including model governance, monitoring, testing, and deployment pipelines
Build and lead an AI operations team, setting standards for quality, performance, and compliance across all AI-driven systems
Partner with AI/ML engineering and data science teams to establish repeatable processes for model validation, versioning, and production handoffs
Develop operational metrics and dashboards that track AI system health, cost efficiency, and business impact
Drive automation of operational workflows to reduce manual toil and accelerate time-to-value for new AI initiatives
Establish risk management and compliance protocols for AI systems, including bias testing, explainability, and regulatory readiness
Collaborate with executive leadership to communicate operational readiness and constraints on AI scaling roadmaps
Evangelize operational best practices across the organization to embed AI maturity into product and business decision-making
Candidate requirements
7+ years of experience in AI operations, MLOps, AI infrastructure, or adjacent roles with clear accountability for scaling AI systems in production
Demonstrated ability to build and manage technical teams focused on operational excellence and process improvement
Deep familiarity with AI/ML workflows, including model training, validation, deployment, monitoring, and retraining cycles
Proven track record translating business strategy into operational roadmaps and building systems that unblock AI velocity
Strong communication skills and ability to work across engineering, product, finance, and business teams to align on priorities and constraints
Experience with cloud platforms (AWS, GCP, or Azure) and modern DevOps / MLOps tooling