Accountabilities

• Product Delivery & Launch Readiness: Own the engineering roadmap from beta through general availability and standalone commercial launch, partnering with product management on requirements, estimates, prioritization, delivery sequencing, and launch-readiness criteria.
• Service Reliability & Performance: Define and validate the service's operational limits, including throughput, latency, rate limiting, availability targets, and service-level objectives (SLOs), ensuring the platform meets production requirements.
• Detection Quality & Accuracy: Establish a continuous detection quality framework using representative test datasets, automated false-positive and false-negative testing, regression suites, and monitoring to maintain and improve detection performance.
• Automation Coverage & Classification: Guide detection capabilities for AI agents, assistants, stealth automation, headless browsers, and other emerging frameworks. Evolve classification systems to provide detailed context, confidence levels, identity verification status, and reason codes.
• Safe Deployment & Continuous Improvement: Manage staged rollouts of detection updates using feature flags, rollback procedures, and measurable release criteria. Incorporate customer feedback and reported detection errors into prioritized engineering improvements.
• Team Leadership & Development: Lead, coach, and develop a small team of senior engineers, manage recruitment as the team grows, and establish clear ownership, accountability, and opportunities for professional growth.
• Engineering Standards & Operational Excellence: Introduce effective engineering practices, including technical specifications, code review standards, repository ownership, and performance benchmarks covering delivery speed, throughput, predictability, failure rates, and recovery time.
• Cross-Team Collaboration: Establish effective working agreements with adjacent engineering teams, clarify shared responsibilities, and progressively reduce dependencies that prevent independent development and deployment.
• AI-Native Engineering Leadership: Lead an agent-first development workflow in which AI tools support specification, coding, review, and security checks, with appropriate human oversight for critical changes. Maintain and improve the tools, instructions, and review frameworks that enable this approach.
• Measurement & Knowledge Sharing: Evaluate the effectiveness of AI-assisted development against established engineering baselines, conduct regular performance reviews, and document lessons learned to inform broader engineering practices.
• API Design & Integration Standards: Ensure all product interfaces follow a contract-first approach, with OpenAPI specifications, automated breaking-change detection, generated documentation and SDKs, and APIs that are accessible to both developers and AI agents.
• Observability & Executive Communication: Own service observability through logs, metrics, traces, and SLOs. Communicate engineering progress, technical trade-offs, detection quality, and launch readiness clearly to leadership, product partners, and customer-facing teams.