Director, AI Engineering

About the role

Axial Search is a specialist executive search firm built for one kind of hire: leaders who help organizations navigate AI transformation.

What The Market Looks Like

We've tracked 1,200+ senior-level AI engineering postings across the US in the last six months, with hiring concentrated in California, New York, and Texas. Professional services, technology, and financial services firms are actively building out their AI engineering teams. Senior-level candidates in this space typically land in the $240k–$410k salary range. The strongest directors bring hands-on fluency with modern ML systems and infrastructure, genuine experience scaling teams through rapid AI adoption, and the judgment to balance engineering rigor with business velocity.

Job Responsibilities

• Lead the design, development, and deployment of AI/ML systems and products that drive measurable business value
• Own the technical strategy for AI engineering initiatives, including architecture decisions, tooling, and infrastructure investments
• Build and mentor an engineering team; set hiring bar, develop talent, and foster a culture of technical excellence and accountability
• Partner with product, data science, and business stakeholders to translate requirements into robust, scalable systems
• Drive best practices around model evaluation, testing, monitoring, and production reliability for AI workloads
• Identify and remove technical blockers; iterate on processes and tooling to unblock the broader AI engineering organization
• Represent engineering voice in AI strategy conversations; communicate trade-offs and technical constraints clearly to non-technical leaders

Candidate Requirements

• 8+ years of software engineering experience, with at least 3 years directly building or shipping ML/AI systems in production
• Proven track record managing and scaling an engineering team; experience hiring, developing, and retaining strong technologists
• Deep hands-on fluency with modern ML tooling, frameworks, and infrastructure (e.g., PyTorch, TensorFlow, cloud ML platforms, MLOps practices)
• Strong foundation in software engineering fundamentals: system design, testing, deployment, and operational excellence
• Track record translating business problems into sound technical strategy and delivering measurable outcomes
• Clear communicator able to explain technical concepts and constraints to cross-functional partners and executives