Director, AI Operations
Boston, MA · In-person · Permanent
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What the market looks like
We've tracked 82 senior-level AI operations postings across the US in the last six months, with heaviest concentration in California, New York, and Texas. These roles span technology, financial services, professional services, healthcare, and manufacturing, reflecting broad organizational investment in operationalizing AI systems at scale. Compensation typically ranges from $200K to $350K annually. The strongest candidates bring hands-on experience scaling ML/AI infrastructure, clear track records owning operational rigor around model deployment and monitoring, and the ability to translate technical complexity into business outcomes for both technical teams and executive leadership.
Job responsibilities
Own the end-to-end operational framework for AI/ML systems—from model deployment pipelines through production monitoring, incident response, and performance optimization
Build and lead the AI operations team, setting hiring strategy, defining roles and accountability, and fostering a culture of operational excellence and continuous improvement
Drive cross-functional alignment between AI/ML engineering, data science, infrastructure, and business stakeholders to ensure smooth model transitions from development to production
Establish operational standards, SLAs, and governance frameworks for model performance, data quality, security, and compliance
Partner with engineering and platform teams to design scalable infrastructure, tooling, and automation that reduce manual overhead and accelerate deployment velocity
Translate operational metrics and system health into clear narratives for executive stakeholders; identify and communicate business impact of operational improvements
Identify bottlenecks and inefficiencies in current workflows; drive process redesign and adopt tooling that improves reliability, reduce toil, and support faster iteration cycles
Candidate requirements
7+ years of experience in AI/ML operations, MLOps engineering, or platform/infrastructure roles supporting ML systems at scale
Demonstrated success building and leading operational teams; comfort with hiring, mentoring, and accountability structures
Hands-on expertise with ML deployment, model monitoring, observability, and incident management in production environments
Track record of designing operational processes, governance frameworks, or automation that improved team velocity or system reliability
Fluency translating between technical detail and business impact; ability to communicate complex operational challenges to non-technical stakeholders
Experience working in or scaling technology organizations; comfort in ambiguous, fast-moving environments