Director, AI Engineering

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What the market looks like

Over the last six months, we've tracked 500+ senior-level postings in AI architecture across the United States, concentrated in California, Texas, and New York. Organizations in technology, financial services, manufacturing, and professional services are actively hiring directors and senior architects to own AI infrastructure, systems design, and technical strategy. Compensation for this cohort typically ranges from $250,000 to $420,000 annually. The strongest candidates bring deep hands-on experience shipping production AI systems, a track record scaling ML infrastructure, and the ability to translate between engineering teams and business leadership.

Job responsibilities

• Own the end-to-end architecture and technical strategy for AI systems, from design through deployment and optimization

• Lead and mentor a team of AI/ML engineers, architects, and platform specialists; set technical standards and code review practices

• Partner with product, data, and infrastructure teams to integrate AI capabilities into production applications and establish governance frameworks

• Drive decisions on model selection, infrastructure platforms, and tooling; evaluate tradeoffs between performance, cost, and scalability

• Build and maintain the AI engineering roadmap; prioritize technical debt, infrastructure improvements, and capability expansions

• Represent AI engineering in cross-functional forums; communicate technical constraints and opportunities to executives and business stakeholders

• Own observability, testing, and quality assurance practices for AI systems in production; reduce technical risk and improve reliability

Candidate requirements

• 10+ years of software engineering or AI/ML infrastructure experience, with at least 5 years in a leadership or architect role

• Demonstrated expertise designing and shipping production AI systems at scale; hands-on fluency with ML frameworks, model deployment, and inference optimization

• Strong track record building and leading technical teams; experience hiring, coaching, and retaining high-performing engineers

• Deep knowledge of cloud platforms (AWS, GCP, Azure) and containerization; familiarity with MLOps tooling, data pipelines, and model serving infrastructure

• Proven ability to translate business requirements into technical architecture; comfort communicating with both engineering teams and non-technical stakeholders

• Experience navigating ambiguous problems in fast-moving environments; comfort making decisions with incomplete information and iterating on solutions