• Lead the build of GenAI-powered product experiences and shared AI platform infrastructure • Design, build, and operate production AI features • Build shared AI platform layer including retrieval infrastructure and eval frameworks • Write LLM applications and integrations with marketing platforms and BI tools • Evaluate model and feature quality using structured eval frameworks • Use AI-driven SDLC tooling for AI and non-AI code • Coordinate with the Personalization team • Document AI system design decisions and operational lessons • Own the observability of AI systems built including latency, cost, quality drift, and error rates

• Build and ship catalog features end-to-end: API design, business logic, data models, and React UI. • Use AI-driven SDLC tooling such as Claude Code as a core part of your daily workflow • Participate in the catalog modernization effort, working on the new stack in parallel with the existing platform • Write and maintain automated tests • Review pull requests, maintain code standards, and contribute improvements to the shared golden path • Collaborate with product and design to refine specs before and during implementation • Instrument services with logging, metrics, and alerts

• Design and ship agentic systems end-to-end — tool calling, sub-agent orchestration, memory, durable state, and failure recovery. • Build evaluation frameworks from the ground up — golden datasets, regression harnesses, and systematic approaches to measuring whether your AI systems actually work. • Own AI features from prototype to production: observable, maintainable, cost-aware. • Work with product and domain engineers to identify where AI has real leverage — and where it doesn't. • Contribute to how we build AI across the org: patterns, tooling, and standards that others build on. • Manage the operational economics of AI systems — latency, token cost, context efficiency — as first-class engineering concerns.

• Develop and maintain applications using Python (back end) and React (front end). • Use AI tools extensively (coding assistants, AI-assisted development) to accelerate delivery and solve complex problems. • Navigate and contribute to large, complex legacy codebases autonomously. • Develop web applications with JavaScript/TypeScript and ReactJS, including unit and end-to-end tests (Playwright). • Work with AWS services (Lambda, S3, DynamoDB, Aurora, among others). • Ensure adherence to coding standards and perform code reviews, ensuring scalable, high-performance applications. • Contribute improvements to DevOps processes and cloud infrastructure when necessary. • Participate in the full software development lifecycle (SDLC).