Manager, AI Solutions
United States · Remote · Permanent
Heads up: this posting is for future opportunities rather than one specific open role. If you apply, we'll add you to our candidate network and may reach out when relevant roles come up.
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What the market looks like
We've tracked 200+ management-level postings in AI operations across the US over the last six months, with the strongest hiring concentrated in California, New York, and Texas. Roles cluster in technology, financial services, professional services, and healthcare sectors. Compensation for this cohort ranges from $160K to $280K annually. The strongest candidates bring hands-on experience operationalizing AI systems—from data pipeline reliability through model deployment and monitoring—combined with the ability to partner across engineering, product, and business teams to translate operational challenges into strategic improvements.
Job responsibilities
Own the operational health and performance of AI/ML systems in production, including monitoring, incident response, and continuous optimization
Lead cross-functional initiatives to improve AI model deployment velocity, reliability, and cost efficiency
Build and mentor a team of AI operations specialists, MLOps engineers, or data engineers focused on infrastructure and automation
Partner with data science, ML engineering, and product teams to identify operational bottlenecks and implement scalable solutions
Drive standardization of AI operations practices, tooling, and documentation across the organization
Manage and optimize cloud infrastructure costs, model serving architecture, and data pipeline efficiency
Establish SLOs, monitoring dashboards, and alerting for AI systems; own operational KPIs and communicate performance to leadership
Candidate requirements
5+ years of experience in AI operations, MLOps, data engineering, or closely related infrastructure roles; 2+ years in a management or lead capacity
Demonstrated hands-on expertise operationalizing AI/ML systems—deploying models, managing data pipelines, scaling inference infrastructure, and troubleshooting production issues
Proficiency with cloud platforms (AWS, GCP, or Azure) and containerization/orchestration tools (Docker, Kubernetes) in production environments
Track record leading technical teams through process improvement, tool selection, and operational transformation initiatives
Strong communication skills; ability to translate technical trade-offs and operational constraints for non-technical stakeholders