Engineering Manager, Automation Intelligence

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Engineering Manager, Automation Intelligence based in Belgium. Lead the development of an innovative intelligence platform designed to distinguish legitimate AI agents and automated traffic from malicious bots and other online threats. In this role, you will manage a high-impact engineering team responsible for delivering advanced detection capabilities, classification systems, and APIs that help enterprises navigate an increasingly automated internet. You will guide the product from beta through general availability and commercial launch, ensuring exceptional detection accuracy, reliability, and scalability. Combining strategic leadership with strong technical judgment, you will establish quality-driven engineering practices and champion an AI-native development model powered by coding agents and automated reviews. You will also build a high-performing team with the autonomy to innovate, deliver independently, and continuously improve. This fully remote opportunity offers direct collaboration with engineering leadership and the chance to shape both a strategically important product and the future of software engineering practices. 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.