Role Overview :

We're looking for a VP 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 :

Strategic and Technical Leadership :

- Jointly define, communicate, and drive the company-wide AI vision, strategy, and technical roadmap in alignment with CoreOps.AI's overall business objectives.

- Provide high-level technical architecture guidance and thought leadership across all AI platforms and products, ensuring consistency, scalability, and innovation.

- Act as the primary AI spokesperson in strategic customer and partner discussions, leveraging deep technical expertise.

Product Portfolio Ownership :

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

- Deliver measurable business outcomes for the owned portfolio while collaborating on cross-product synergies with other product and engineering leaders.

Forward Deployment Engineering :

- Lead and own all forward deployment engineering activities for the AI product suite : discovery calls, technical demonstrations, and solution architecture.

- Serve as the escalation point and final technical authority in forward deployment engagements, ensuring high win rates and strong customer relationships.

Team Leadership and People Development :

- Provide visible leadership, mentoring, and coaching to the broader AI organization, with emphasis on technical talent development, knowledge sharing, and career growth.

- Foster a high-performance, collaborative culture; actively contribute to hiring, retention, and team morale across the AI function.

Ideal Candidate Qualifications :

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

- 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 that meet stringent requirements for scalability, latency, cost, and observability in production environments.

- Experience in forward deployment engineering, including technical pre-sales, solution architecture, and customizing AI systems for enterprise customers.

- Demonstrated success building, mentoring, and scaling high-performing AI engineering teams (50+ engineers) while maintaining exceptional code quality, velocity, and retention.

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

- Startup mindset with comfort operating in ambiguity, high ownership, rapid iteration, and a bias toward measurable business impact through technical execution.