Role Overview:

We're looking for a Vice President of AI Engineering to lead our AI organization into its next phase of scale and impact. This is a high-ownership role for a technically deep, customer-obsessed leader who thrives in a fast-moving startup environment and wants to own both strategy and execution across our most critical AI products.

What You'll Own:

- Jointly define, communicate, and drive the company-wide AI vision, strategy, and technical roadmap.

- Provide high-level technical architecture guidance and thought leadership across all AI platforms and products.

- Act as the primary AI spokesperson in strategic customer and partner discussions.

- Own full end-to-end accountability for a major product portfolio, covering vision, roadmap, prioritization, delivery, quality, and customer adoption.

- Lead and own all forward deployment engineering activities for the AI product suite.

- Partner closely with other product leaders to eliminate duplication and accelerate delivery.

- Provide visible leadership, mentoring, and coaching to the broader AI organization.

- Apply strong project management discipline to ensure on-time, high-quality delivery of complex AI initiatives.

What Success Looks Like:

- Architect and deliver production-grade AI systems that achieve industry-leading standards in scalability, reliability, latency, and cost efficiency.

- Drive technical excellence across the engineering organization through agentic SDLC.

- Build and scale high-performing AI engineering teams that consistently ship complex, high-impact features on time.

- Deliver measurable business impact through your product portfolio.

Qualifications:

- 10+ years of progressive software engineering experience with at least 5+ years leading large-scale AI/ML organizations.

- Deep hands-on expertise in modern AI/ML technologies, including large language models, computer vision, MLOps pipelines, model optimization, distributed training, and high-performance inference infrastructure.

- Proven track record of architecting and shipping complex, reliable AI products.

- Experience in forward deployment engineering, including technical pre-sales and solution architecture.

- Demonstrated success building, mentoring, and scaling high-performing AI engineering teams (50+ engineers).

- Expertise in cloud-native platforms (AWS, GCP, or Azure), distributed systems, CI/CD pipelines, and infrastructure-as-code.

- Startup mindset with comfort operating in ambiguity, high ownership, and a bias toward measurable business impact.