Manager, AI Solutions

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.

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What the market looks like

We've tracked 2,300+ management-level postings in AI architecture across the US over the last six months, with New York, San Francisco, and Texas leading hiring volume. Demand is strongest in IT services, technology, professional services, and financial services sectors, where organizations are building internal AI capabilities at scale. Compensation for this cohort ranges from $200k to $390k annually. The strongest candidates bring hands-on experience shipping AI systems, fluency across the modern ML stack, and a track record of building and mentoring technical teams through ambiguous transformation work.

Job responsibilities

• Own the design and delivery of AI architecture strategies that align technical feasibility with business objectives, from discovery through production deployment

• Lead and mentor a team of AI/ML engineers, architects, and data specialists—setting technical direction, unblocking work, and developing talent

• Partner with product, engineering, and business leadership to identify high-impact AI use cases, scope feasibility, and drive roadmap prioritization

• Build and maintain AI systems architecture—including model pipelines, inference infrastructure, monitoring, and governance frameworks—ensuring scalability and reliability

• Drive adoption of AI best practices across teams: experimentation discipline, model evaluation rigor, responsible AI principles, and MLOps maturity

• Manage technical debt and infrastructure decisions that balance speed-to-market with long-term system health and cost efficiency

• Communicate architecture trade-offs, technical risk, and capability roadmaps to non-technical stakeholders and executive sponsors

Candidate requirements

• 7+ years of hands-on experience building and shipping AI/ML systems, with 2+ years leading technical teams or owning major architectural decisions

• Deep technical fluency across the modern ML stack: model training and evaluation, feature engineering, inference serving, monitoring, and deployment infrastructure

• Proven ability to scope AI problems, run experiments rigorously, and communicate when and why AI is or isn't the right tool

• Experience navigating the full lifecycle of AI transformation—from discovery and prototyping through operationalization and governance

• Track record of mentoring engineers, building high-performing teams, and influencing technical strategy across functions

• Comfort working in ambiguity, translating business problems into technical requirements, and driving decisions under incomplete information