• Build and ship fullstack features across multiple AI-first products, including agentic music production and audio ML experiences like voice conversion, synthesis, and composition • Work directly with PMs and designers to take rough ideas to shipped product quickly, owning the full arc from ticket to production • Integrate and serve audio ML models in production and build the creator-facing surfaces that bring them to life • Develop agents and AI-powered workflows that do real, meaningful work inside the product • Use agentic coding tools (Claude Code, Cursor, Codex) as a core part of your daily development workflow • Own features end to end, prototype, production, reliability monitoring, and iteration • Uphold code quality and architectural standards even when moving fast, reading diffs line by line and writing tests for production-facing PRs • Contribute to a codebase that improves over time, with integrated reliability monitoring and a measurable weekly impact on shipped features

• Build AI-powered tools into engineers’ day-to-day workflows (e.g., Claude, VS Code, GitLab, Datadog, internal documentation, Slack chatops). • Implement and evolve inference and tool-calling pathways using Claude models on Amazon Bedrock, LiteLLM, and MCP/tool gateways within Omada’s secure networks. • Partner with teams across Engineering to define AI-augmented SDLC patterns across planning, coding, testing, and operations. • Prototype and harden Slack-based chatops experiences that safely orchestrate internal services through business-level agentic harnesses. • Instrument and monitor AI usage, performance, and cost to help the team and engineering leadership understand our impact and our organizational maturity. • Collaborate with security, compliance, and infrastructure teams to ensure that AI workflows are designed to handle PHI and other sensitive data safely. • Create reusable examples, templates, and internal libraries that make it easy for teams to adopt AI-powered workflows without bespoke implementations. • Contribute to training, documentation, and office hours that help engineers, PMs, and designers become confident AI users and partners.

• Design and build software testing tools that integrate with engineers’ day-to-day environments (e.g., Claude, VS Code, GitLab, Datadog, internal documentation, Slack chatops). • Build and maintain automated test infrastructure that supports mobile, web, and backend engineering teams. • Define AI-augmented software testing patterns and quality gates in our release management system • Instrument and monitor AI usage, performance, and cost to help the team and engineering leadership understand our impact and our organizational maturity. • Collaborate with security, compliance, and infrastructure teams to ensure that AI workflows are designed to handle PHI and other sensitive data safely. • Create reusable examples, templates, and internal libraries that make it easy for teams to adopt AI-powered quality workflows • Contribute to training, documentation, and office hours that help engineers, PMs, and designers become confident automated test users and partners.

• Build and enhance modern web applications and APIs • Develop frontend experiences using React and TypeScript • Implement backend services and integrations using Python • Work with PostgreSQL, DynamoDB, and distributed systems • Contribute to scalable, event-driven architectures • Use AI coding assistants and agentic development workflows as part of daily engineering work • Help prototype and improve AI-driven engineering workflows • Work with AWS cloud-native infrastructure • Participate in CI/CD and automated testing workflows