Principal Engineer, Payments

As a Principal Engineer on the Payments Technology and Engineering team, you'll set technical direction and design best-in-class solutions and do hands-on engineering for products that process millions of B2B payments. The role blends hands-on software development, architecture, coaching, and cross-functional partnership with Product, Architecture, Database, DevOps, Performance Engineering, and Operations — this is not a purely advisory position. You'll bring an AI-first mindset to everything from daily coding to system design, treating agentic AI tooling as a default part of the SDLC rather than an occasional aid. This is a senior individual-contributor role — you'll drive impact through system design, architecture, mentorship, and cross-team influence.

What You'll Do

Technical Leadership & Architecture

Define and drive the Payments platform's technical approach, system design decisions, and engineering best practices.
Advance platform scalability, reliability, and observability through API-first and event-driven design, distributed systems, cloud-native architecture, and fault-tolerant patterns (e.g., circuit breakers, retries with backoff, etc.).
Partner with Solution and Enterprise Architects, and with developers, to determine optimal designs for architecture, efficiency, maintainability, and scalability across new and existing capabilities.
Collaborate with Product, Business Operations, and Engineering leaders to align technical solutions with business needs and shape engineering roadmaps.

AI-First Engineering

Adopt an agentic AI-based SDLC as the default approach to building software — using AI coding agents to scaffold, implement, test, and review code, not just as an occasional assist.
Work fluently with AI harness / coding-agent tools (e.g., Cursor, Claude Code, VS Code with GitHub Copilot) as part of your daily workflow and help establish team-wide standards and best practices for their use.
Champion AI-driven engineering practices — intelligent automation, predictive analytics, and self-healing systems — to improve development efficiency and system resilience.
Identify and implement AI-based solutions for Payments product initiatives, from proof of concept through production.
Model and promote an AI-first mindset across the team — evaluating new AI tooling, sharing what works, and pushing the team toward higher leverage.

Quality, Security & Delivery

Solve problems, debug, and optimize code for performance and security, with attention to the compliance and regulatory standards that apply to payment processing (e.g., PCI-DSS).
Own code quality and unit test coverage; partner with QA to design and review test cases so the platform meets customer expectations.
Perform code reviews and approve pull requests, ensuring code follows documented standards and best practices.
Own the health of the software delivery pipeline and guide the team in improving it.

Hands-On Development, Mentorship & Collaboration

Stay hands-on — writing code, building proofs of concept, and pairing directly with engineers — while also setting architectural direction; this role is a blend of building and leading, not one or the other.
Mentor and coach developers, with particular focus on senior engineers, through code review, design discussions, and technical guidance.
Partner closely with Product and Operations to ensure technical solutions align with business needs and day-to-day operational realities.
Influence engineering culture and product solutions — within the team and company-wide — through thought leadership.
Drive full lifecycle application development: research technical options for new features, provide guidance based on findings, and build proofs of concept to validate the best approach.

What We're Looking For

Required

Demonstrated agentic AI-first approach to the SDLC, with hands-on fluency in AI coding-agent tools such as Cursor, Claude Code, or VS Code with GitHub Copilot.
Knowledge and experience in AI agent development using tools and frameworks such as LangChain/LangGraph, Databricks, vector databases, and open- and closed-weight LLMs.