Principal Software Engineer - Remote

As a Principal Agentic Software Engineer, Quality Systems, you are a hands-on, product-minded, customer-focused full-stack engineer who sets technical direction across multiple products and platforms and personally builds the primitives other teams depend on. This is a builder's seat with a quality center of gravity. You direct AI coding agents through real, multi-step work, and you own the harder half of that equation: knowing when the output is right. As we ship faster with agents, the engineers who can tell correct code from plausible-looking code become the ones everything else rests on. You decide what becomes shared infrastructure, make the build-vs-buy calls that shape our roadmap, and build the evaluation and test systems that let the rest of engineering move quickly without breaking trust.

Your work reaches millions of people. We build the travel and rewards platforms behind major banks, membership organizations, and travel brands, where members book real trips with real money and points, so trust is the product. Operating with broad autonomy and cross-organizational accountability, you will lead the one-way-door decisions in your areas, balance short-term partner requests against long-term member trust, and set the operating rhythms, launch gates, and quality standards that let dependent teams move faster while protecting it.

What You'll Own

• Technical direction: Set direction across multiple products and platforms, including AI infrastructure, data platforms, and shared primitives, and personally build the components that increase leverage for teams beyond your own.

• Agentic engineering practice: Co-define our prompt libraries, MCP server patterns, quality gates for AI-generated code, and human-agent collaboration patterns, then get them adopted.

• The quality bar for agent output: Define how AI-generated code gets reviewed and validated for correctness, security, performance, and maintainability, and build the tooling that makes that judgment repeatable rather than heroic.

• Test and evaluation infrastructure: Architect the automated testing, regression, and AI evaluation systems that other teams build on, including eval frameworks, guardrail dashboards, and the harnesses that catch silent failures a green test suite would miss.

• MCP integrations: Architect Model Context Protocol integrations that give AI agents secure, governed access to internal APIs, data sources, and platform services across teams.

• Agentic quality workflows: Define patterns and platforms that automate testing, documentation, code-review triage, and deployment checks at scale.

• Standards and simplification: Drive build-vs-buy decisions and convergence on strategic standards that reduce complexity rather than add to it.