VP, AI Engineering

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What the market looks like

We've tracked 10 senior-level MLOps postings in the last six months across the US, with concentrations in California, Texas, and New York. Organizations building production AI infrastructure are competing hard for leaders who can architect and scale model deployment pipelines, bridge data engineering and ML teams, and ship systems that handle real-world latency and reliability demands. Compensation in this cohort lands between $210K and $330K base. The strongest candidates bring hands-on MLOps delivery experience—not just theoretical knowledge—paired with the ability to own infrastructure decisions, mentor technical teams, and translate model requirements into production constraints.

Job responsibilities

• Own the design and scaling of MLOps infrastructure, including model serving, monitoring, and retraining pipelines that support production AI systems

• Lead cross-functional teams (data scientists, ML engineers, infrastructure engineers) to establish reproducible, auditable workflows for model development and deployment

• Drive adoption of MLOps tooling and best practices—version control, containerization, orchestration, and observability—across the organization

• Partner with data science and product teams to translate business requirements into technical specifications for model inference, latency, and cost targets

• Build and mentor a team of MLOps engineers and ML infrastructure specialists, establishing technical standards and career paths

• Manage infrastructure budgets, cloud costs, and resource allocation for model training and serving workloads

• Establish monitoring, alerting, and incident response practices to ensure model performance, data quality, and system reliability in production

• Evaluate and integrate new MLOps platforms and tools, balancing innovation with organizational stability and team capability

Candidate requirements

• 7+ years of hands-on MLOps, ML infrastructure, or platform engineering experience, including at least 3 years in a leadership or senior individual contributor role

• Proven track record shipping production ML systems at scale—model serving, retraining pipelines, feature stores, or similar infrastructure components

• Deep familiarity with containerization, orchestration (Kubernetes, Airflow, or equivalents), and cloud platforms (AWS, GCP, or Azure)

• Strong systems thinking: ability to reason about latency, scalability, cost, and reliability trade-offs in complex technical environments

• Experience building and leading technical teams, with demonstrated ability to mentor engineers and raise the bar on technical rigor

• Comfort communicating with both technical and non-technical stakeholders—translating infrastructure decisions into business impact