Manager, Software Engineering

Role overview

Lead the development and operations of large-scale, AI-enabled enterprise SaaS web applications. You will manage a distributed engineering team that builds and runs customer-facing platforms hosted on AWS, ensuring scalability, reliability, and security in a fast-paced, product-driven environment.

Responsibilities

• Lead design, development, and delivery of enterprise-grade customer-facing web applications using microservices and cloud-native architectures.
• Provide technical leadership via architecture governance, design reviews, and code reviews.
• Manage and mentor a distributed team of software and quality engineers; foster accountability, growth, and strong engineering culture.
• Own production operations: incident response, on-call, and post-incident reviews (“you build it, you run it”).
• Drive adoption of Generative AI tooling and modern engineering practices to improve productivity and quality.
• Partner with Product Management and Design to translate business requirements into scalable solutions.
• Improve operational excellence with observability, automation, resilience, and alignment to SLAs/SLOs.
• Continuously enhance engineering processes, system reliability, and delivery efficiency.

Requirements

• 10+ years software engineering experience, including 3+ years in engineering management or team leadership.
• Proven experience delivering enterprise-scale SaaS with customer-facing impact.
• Strong AWS expertise across compute, storage, networking, security, and monitoring.
• Deep knowledge of distributed systems, microservices architecture, and cloud-native design.
• Frontend proficiency with React and backend expertise with Ruby on Rails (or equivalent).
• Experience with PostgreSQL or similar relational databases.
• Demonstrated production ownership in “you build it, you run it” environments.
• Strong leadership, communication, and cross-functional collaboration in Agile/Scrum environments.

Nice-to-haves (implied)

• Experience integrating modern Generative AI capabilities into product workflows.
• Strong background in resilience, automation, and reliability engineering practices.