VP, AI Engineering

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.

Axial Search is a specialist executive search firm built for one kind of hire: leaders who help organizations navigate AI transformation. Apply today to express your interest in roles like this one.

Visit our website to learn more about our process and explore free tools for your job search, including our live job market dashboard with salary, skills and hiring trend data from thousands of AI transformation roles.

What the market looks like

We've tracked 200+ senior-level AI engineering postings in the US over the last six months, with hiring concentrated in California, New York, and Texas across professional services, technology, financial services, and manufacturing. Compensation for this cohort typically ranges from $200K to $490K base salary. The strongest candidates in this space bring hands-on experience shipping production ML systems, a track record of scaling engineering teams through periods of rapid AI adoption, and the ability to translate between research-grade AI work and operational delivery—combining deep technical credibility with organizational leadership at the VP or director level.

Job responsibilities

• Own the technical strategy and roadmap for AI/ML engineering, translating business priorities into product and platform capabilities that scale across the organization

• Build and lead a high-performing AI engineering team—hiring talent, setting technical standards, and creating an environment where engineers ship reliable systems in production

• Drive the architecture and implementation of ML platforms, data pipelines, and inference infrastructure that support the organization's AI initiatives

• Partner with product, research, and data science teams to integrate AI/ML models into customer-facing and internal applications, owning quality, latency, and reliability

• Establish MLOps practices, monitoring, and governance frameworks that ensure models perform in production and meet compliance requirements

• Represent engineering in cross-functional AI transformation discussions, advising leadership on technical feasibility, resource trade-offs, and capability gaps

• Evaluate and integrate third-party AI tools, platforms, and vendor relationships that accelerate time-to-value for the engineering organization

Candidate requirements

• 10+ years of software engineering experience, with at least 5 years building, shipping, and operating machine learning systems in production environments

• Demonstrated experience leading and scaling engineering teams (typically 5–30+ engineers) through periods of rapid growth and technical change

• Deep hands-on expertise in ML infrastructure, model deployment, experiment tracking, or MLOps—comfortable discussing tradeoffs in training pipelines, inference latency, and model governance

• Track record of translating ambiguous AI/ML requirements into concrete engineering roadmaps and shipping systems that deliver measurable business value

• Experience working cross-functionally with data science, product, and business teams to align technical decisions with organizational goals

• Comfortable in a remote, distributed environment and skilled at building alignment asynchronously across geographies and functions