We are looking for a Senior Full-Stack Engineer to design, build, and ship features end-to-end across one of our core product teams — from the interface a user touches, through the API, down to the data. You own meaningful parts of the product front to back: shaping the approach with your team, building both the UI and the services behind it, testing them, and running them in production. This is a hands-on role: most of your day is spent writing, reviewing, and debugging real code across the stack, with the autonomy to make sound technical decisions and the judgment to know which ones to raise.

You care about more than making it work. You think about edge cases, failure modes, performance — both on the server and as the user perceives it — accessibility, and the engineers who'll read your code next. You collaborate closely with product managers, QA, and fellow engineers to turn ambiguous goals into reliable software, and you push back early when requirements don't add up. You bring an AI-first working style — using AI tooling daily to move faster across design, coding, and review — with the judgment to catch where its output falls short. You’re also comfortable working hands-on with LLM/agent APIs — prompt and context design, retrieval, and orchestrating agent workflows — and understand their behavior and failure modes in production.

This is not a role for someone who lives on only one side of the stack. We need an engineer who's genuinely comfortable building and owning a feature from the component to the database, takes it from ambiguous intent to shipped, reliable software, and raises the bar for the team around them.

Key Responsibilities - What this person will own and do day-to-day

Feature ownership (front to back)

• Build and own features end-to-end — UI, API, and data — from design discussion through implementation, testing, deployment, and production support.
• Translate product requirements and architectural direction into working, maintainable code across the stack.
• Break larger pieces of work into shippable increments and sequence them sensibly.

Frontend craft

• Build accessible, responsive UIs with a modern framework (React, Vue, or similar) in TypeScript.
• Make sound component-architecture and state-management decisions, and keep the front end maintainable as it grows.
• Care about perceived performance and the actual user experience — not just that it renders.

Backend, data, and APIs

• Design and build services and APIs (REST/GraphQL or event-driven) with sensible data models across relational and non-relational stores.
• Write clean, well-tested production code; handle failure deliberately with sound error handling and observability.
• Build and maintain integrations with third-party platforms and reason about their failure modes.

Code quality and working within the architecture

• Review PRs thoroughly — catching correctness, clarity, and maintainability issues, not just style.
• Implement against the boundaries, contracts, and patterns the architecture defines, flag when a contract or requirement doesn't hold up, and escalate the decisions that aren't yours to make.
• Refactor and pay down technical debt deliberately, where it buys real leverage.

Cross-functional collaboration

• Work directly with Product and Design on scope, feasibility, and trade-offs inside the current iteration.
• Collaborate with your tech lead and fellow engineers to keep the system coherent as it grows.

AI-augmented engineering

• Use modern AI tooling (IDE agents, CLI assistants, MCP servers) as a daily working layer across design, coding, and review.
• Apply judgment to AI-assisted output — treat it as acceleration, not a substitute for understanding the code you ship.

Required Qualifications

• 5+ years of professional software engineering experience building and operating production systems, working across both frontend and backend.
• Strong fluency in JavaScript/TypeScript and Python; comfortable owning non-trivial code in both.
• Frontend depth: production experience with a modern framework (React, Vue, or similar) in TypeScript, plus a working grasp of responsive layout, accessibility, and browser performance.
• Backend depth: building services and APIs (REST/GraphQL or event-driven) with solid data modeling across relational and non-relational stores.
• Hands-on experience building services on AWS — using core compute, data, and identity services (e.g., ECS/EKS, RDS/S3, IAM) and reasoning about their behavior, not just calling them.
• Comfortable with modern CI/CD and Git workflows — GitHub Actions (or equivalent), PR-based review, branching, and automated testing as part of how you ship.
• Hands-on experience building production features on top of LLM/agent APIs (e.g., AWS Bedrock, OpenAI, Anthropic) — prompt/context design, retrieval, and orchestrating agent workflows, with judgment about cost and failure modes.
• Strong production instincts: writes testable code, handles failure deliberately, and can debug and profile a live system — front and back — under real conditions.
• Reads and works within an existing architecture — implements against defined contracts and guardrails, and knows when to challenge them vs. when to escalate.
• AI-first working style: hands-on daily use of AI tooling (Claude, Cursor, Copilot, or similar) with the judgment to verify its output.
• Senior judgment and autonomy: takes ambiguous requirements to ship software with minimal hand-holding, and owns the result.
• Advanced English; comfortable in a fully remote, distributed team.

Nice to have

• Strong design and UX sensibility; experience working from a design system and partnering closely with designers.
• Experience integrating with multiple third-party platforms in production (Slack, GitHub, Atlassian, Google Workspace, Salesforce, Stripe, MCP servers).
• Exposure to Infrastructure as Code (Terraform, CloudFormation/CDK, or Pulumi) — able to read and extend it, even if not owning it.
• Experience working in multi-tenant or enterprise-facing products with real security and data-residency constraints.
• Familiarity with observability tooling (Datadog, Grafana, Sentry, or similar) and using it to close production feedback loops.
• Experience mentoring engineers or leading the implementation of a sizable feature