What You'll Own
Technical direction: Define architecture and technical strategy with the CTO, Product and AI leadership. Make real tradeoffs between speed, simplicity, reliability and long-term scale. Stay deep enough to lead architecture reviews, surface hidden risks, and tell the difference between an impressive demo and a dependable production system.
An enterprise-grade agentic platform: Agent orchestration, planning and tool use governed knowledge and policy representation retrieval, context and memory evaluations human review and escalation permissions, identity, security and auditability reliability and observability cost, latency and model performance enterprise integrations.
The R&D organization: Design the structure for the company's next stage. Recruit exceptional engineers and engineering leaders at a consistently high bar. Grow managers and senior ICs who own major domains independently. Set clear expectations, feedback and performance standards – and build a leadership bench so the org doesn't depend on a handful of people.
Autonomy with accountability: Define outcomes, decision boundaries and interfaces, then push decisions as close to the problem as possible. Autonomy that produces better and faster decisions – not ambiguity, duplicated work, or diffused responsibility.
Velocity and quality together: An operating model that moves fast without normalizing instability. Better planning, testing, deployment, observability and incident learning. AI-native development as real leverage for every engineer, with clear standards for security, correctness and human judgment.
Business impact: Shape the roadmap with Product rather than receiving requirements. Join strategic customer conversations where technical leadership builds trust and unblocks adoption. Operate as a company leader, not only a function leader.
Significant experience building complex software systems – backend, cloud, distributed systems, data platforms or enterprise architecture
Several years leading engineering organizations through managers and senior technical leaders across multiple teams
A track record of scaling an org while preserving technical quality, speed, accountability and the talent bar
Hands-on experience building or operating AI / ML / LLM / agentic systems in production
Real understanding of what makes AI agents reliable: evaluations, orchestration, context, model behavior, observability, permissions, security, failure handling
Credible contribution to architecture without becoming the approval bottleneck
Management treated as a craft: hiring, coaching, feedback, performance management, org design
Strong product and business judgment, and clear communication with engineers, executives and customers
Comfort in an early-stage environment where product, org and category are still being shaped
