About the job
Aerones is on the lookout for a dynamic and seasoned Engineering Manager to spearhead engineering initiatives across multiple product teams focused on a unified platform. In this pivotal role, you will champion engineering excellence, cultivate a collaborative environment, and mentor teams in creating state-of-the-art solutions. A core aspect of this position involves facilitating AI-native practices within your teams—implementing effective processes, tools, and training to ensure that engineers harness AI coding agents (such as Claude Code, Codex, etc.) as their main development tools. A deep understanding of AI-augmented engineering is essential for setting your teams up for success. This role transcends pure people management; we seek a hands-on leader who can engage in architectural decisions, troubleshoot production issues, and dive into code when necessary.
Key Responsibilities:
- Multi-team Leadership: Oversee engineering execution across several product teams contributing to a shared platform. Align priorities, resolve cross-team dependencies, and maintain consistent engineering standards.
- AI-native Engineering Adoption: Enhance and implement processes for specification-driven, agent-augmented development workflows. Revise standards for context engineering and AI-assisted code reviews. Monitor adoption metrics and identify areas needing additional support.
- Tooling and Enablement: Assess, select, and provision AI coding tools for teams. Manage tool budgets and usage to ensure engineers have optimal AI development environments.
- Training and Upskilling: Develop and execute training programs that transition engineers from traditional to AI-augmented development methodologies.
- Integration Architecture: Contribute to defining system boundaries, API contracts, and integration architecture across platform components to ensure cohesive and interoperable services.
- Hiring and Team Building: Drive the engineering hiring process in alignment with budgetary constraints. Oversee onboarding and performance evaluations of both internal and outsourced engineers.
- Capacity Planning: Manage engineering capacity planning to forecast resource needs, adjust team composition based on product priorities, and balance workloads across teams.
- Establish and uphold development best practices, including quality gates for AI-generated code (verification, testing, review standards).
- Monitor engineering KPIs, including AI adoption metrics.

