Staff Engineer, Embedded Finance & AI

Skills

About the Role

You will design and evolve the embedded finance platform architecture, incorporate AI assisted engineering practices, and drive platform initiatives from RFCs to production. You will partner with Card Engineering Managers to align platform direction with product delivery needs, identify systemic issues, and mentor engineers. You will build reusable AI tooling, define standards, and improve observability and reliability to scale with business growth.

Requirements

• 10+ years of software engineering experience with a track record of technical leadership at Staff or Principal level
• Deep experience in distributed systems API platform architecture or financial infrastructure at scale
• Proven ability to drive large complex technical initiatives across multiple teams without direct authority
• Strong systems thinking about fault tolerance observability data consistency and performance at scale
• Clear and compelling communicator able to write RFCs lead design reviews and present technical direction to engineering and executive audiences
• Genuine interest in AI and LLM tooling and its application to software engineering workflows
• Hands on experience with AI assisted development tools such as Claude Code GitHub Copilot Cursor
• Proficiency with our main tech stack TypeScript Node.js NestJS and AWS

Responsibilities

• Define and drive the technical roadmap for the embedded finance platform
• Lead AI assisted engineering across Product Engineering teams from AI augmented development workflows to automation of repetitive processes
• Collaborate with Card Engineering Managers to align platform direction with product delivery needs
• Identify systemic engineering quality issues and build plans to address them
• Serve as a technical anchor for senior engineers through design reviews setting standards and mentoring
• Lead architecture modernization of embedded finance systems including service boundaries data models and API contracts
• Identify and resolve scalability bottlenecks before they become production constraints
• Drive platform initiatives end to end from RFC through production delivery and stability
• Audit technical debt and prioritize remediation alongside product delivery
• Advocate for and lead refactors with long term impact on velocity and reliability
• Lead AI adoption strategy at the engineering team level including tooling patterns and SDLC workflows
• Build and maintain reusable AI tooling and prompt patterns
• Track and report on AI adoption signals across Product Engineering teams

Benefits

• Insurance coverage after probation
• Reap Card stipend
• Use of AI tools at work with space to learn and grow
• Autonomy to act as technical owner of a key partnership