Set product direction: Define the vision and priorities for the ASAPP portfolio. Make clear choices about where we can create durable differentiation and translate customer, market, and technology shifts into a focused roadmap.

Turn technology into products: Partner with Research, Engineering, Delivery, and Security to convert advances in AI into products that are intuitive, reliable, and commercially valuable to customers.

Productize customer needs: Work directly with customers to understand their most important problems, find the patterns that repeat across enterprises, and convert those patterns into reusable product and platform capability.

Own products end to end: Lead products from incubation and early product-market fit through production deployment, adoption, expansion, and scale. Establish clear ownership, success metrics, and accountability across the portfolio.

Deliver customer outcomes: Make sure customers can deploy ASAPP’s GenerativeAgent and reach measurable value. Hold the bar on reliability, integrations, usability, configurability, adoption, speed to value, and business results.

Decide from production data: Stay close to how products and capabilities actually behave in production. Use telemetry, customer feedback, operational data, and business metrics to find issues, improve quality, and set investment priorities.

Hold the AI quality bar: Set a high standard for AI quality, evaluation, safety, reliability, latency, and observability. Partner with technical teams on the tradeoffs between speed, quality, cost, and scale.

Focus and execute: Establish clear priorities and make the hard tradeoffs. Say no when it is warranted, concentrate teams on what matters most, and partner with Engineering to ship with urgency, stability, and technical excellence.

Shape how we sell and deploy: Work with Sales, Marketing, Customer Success, and Delivery to refine positioning, packaging, segmentation, and product strategy based on what customers actually buy, deploy, adopt, and expand.

Lead across functions: Build trust across Research, Engineering, GTM, Delivery, and company leadership, and align the organization around product priorities and customer outcomes.

Build the team: Recruit, develop, and retain outstanding Product and Design talent. Build an organization with strong leaders, clear accountability, and excellent judgment, while staying personally engaged in the decisions that matter.

Enterprise AI product experience: Significant experience building and scaling AI products that enterprise customers run in production. Strong familiarity with modern generative AI, agentic systems, and workflows powered by AI.

Technical product depth: Able to engage meaningfully with researchers and engineers on AI systems, orchestration, knowledge and retrieval, evaluation, safety, latency, reliability, data, and platform architecture. You do not need to be a researcher, but you cannot outsource technical product judgment.

Builder mentality: A record of personally building products through ambiguity, finding product-market fit, and scaling from there, rather than inheriting mature products or businesses.

Enterprise product judgment: Experience in complex B2B environments with deep integrations, many stakeholders, demanding production requirements, and deployments that run for years.

Customer and commercial instinct: Comfortable with senior enterprise customers, and able to tell the difference between what one customer asks for and what the broader market will repeatedly value, buy, and deploy.

Productization ability: Demonstrated ability to find the common pattern across customer needs and turn it into a scalable product or platform capability.

Operating rigor: Strong prioritization, metrics orientation, and accountability. Comfortable making difficult tradeoffs and focusing teams on a small number of important outcomes.

Leadership: Proven ability to attract, develop, and lead exceptional Product and Design talent while remaining personally engaged in important product decisions.

Entrepreneurial spirit: Thrives in constant change, limited structure, rapid learning, and evolving product market fit.

Relationship builder: Can build trust across Research, Engineering, Sales, Delivery, and the executive team, and bring those groups together around clear product choices.

Enterprise customer experience, contact center, or communications products.

Voice AI, conversational AI, or AI systems that operate in real time.

Developer platforms, extensible enterprise platforms, or complex enterprise workflow products.

Working closely with AI research teams and bringing research advances into production.

Technology partnerships and ecosystem integrations.