Years of software development experience, including designing and delivering cloud-native solutions in production
Demonstrated experience leading technical initiatives, influencing stakeholders, and mentoring engineers
A small CTO mindset: comfortable running several workstreams in parallel, making architectural trade-offs, and owning outcomes end to end rather than individual tasks
Fluency with AI coding agents and assistants (e.g., Claude Code, GitHub Copilot, Cursor) as a core part of daily engineering work: designing agentic workflows, breaking complex problems into tasks agents can execute well, and driving the work through to completion, with strong engineering judgment and full ownership of the output
A clear view of what production ready means across security, reliability, performance, cost, and maintainability, and the discipline to hold AI-assisted work to that bar before it ships
The technical depth to make sure AI is building the right tool at the right layer: challenging designs, catching the wrong abstraction early, and correcting course before rework gets expensive
Strong foundation in Programming (Python, TypeScript/JavaScript, and/or Go), with the ability to architect, review, and validate production-grade applications and services
Proven ability to architect and operate scalable, secure cloud-native applications, preferably on AWS, with a strong grasp of core cloud concepts
Strong command of modern web application and API architecture, including React, Node.js, and the design of secure, scalable, and performant RESTful APIs
Strong understanding of SQL and NoSQL database concepts, data modeling, performance, and trade-offs; experience with PostgreSQL, Redis, MongoDB, or similar
Experience designing and operating CI/CD pipelines and GitOps-based delivery (GitHub Actions, Jenkins, ArgoCD, CircleCI, or similar), and a strong understanding of infrastructure as code (Terraform, CloudFormation, Ansible, or similar) and the systems being automated, whether the code comes from you or from an AI agent
Skilled in GitHub workflows, version control, code review (including reviewing and validating AI-generated code), and technical documentation