Lead Software Engineer - AI-Augmented Full Stack Development

• Leverages AI coding tools (Cursor, Claude Code, Copilot, n8n) to accelerate delivery while maintaining deep knowledge of databases, authentication, CI/CD pipelines, cloud infrastructure, and system architecture. 
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

• You'll design and deliver trusted technology products in a secure, stable, and scalable way. You'll collaborate with data scientists, product managers, and business owners to solve real problems, making architectural decisions and leveraging AI tools for rapid execution.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Experience in building production software systems across the full stack
• Deep understanding of system architecture: databases (relational/NoSQL), authentication/authorization, API design, cloud infrastructure (AWS/Azure), containerization, CI/CD pipelines
• Hands-on experience with AI coding tools (Cursor, Claude Code, Copilot, or similar) - you know when they accelerate work and when human judgment is critical
• Proficiency in modern languages and frameworks (we use Java/Spring Boot, React/Angular, but care more about your ability to learn and deliver)
• Experience evaluating and integrating AI/LLM capabilities into applications
• Strong judgment about trade-offs: when to code vs. use low-code/orchestration tools, when to optimize vs. ship
• Understanding of agile methodologies, application resiliency, and security practices
• Ability to communicate technical decisions clearly to both technical and business stakeholders
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.

  • Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices