• 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

• Design and build platform capabilities that deliver reliable, high-value features with safety, precision, and user focus. • Collaborate closely with product managers, designers, and fellow engineers to create new features and improve existing ones. • Approach the platform as a product: maintain a clear understanding of customer needs, identify friction points, and take ownership of outcomes with metrics-driven decision making (KPIs, OKRs, and North Star metrics). • Use data to inform decisions, measure impact, and drive improvements through empirical evidence. • Take ownership across all phases of development, from design to production, and monitor system performance in live environments. • Continuously improve the flow of work from idea to production, using data to identify and remove friction. • Write clean, efficient, and well-documented code following best practices. • Build and maintain APIs, and integrate with third-party services. • Ensure application performance, quality, and responsiveness across platforms. • Participate in code reviews, testing, and debugging to maintain high-quality deliverables. • Mentor other engineers and contribute to their growth. • Contribute to architectural decisions and help define technical standards and best practices. • Stay up to date with emerging technologies and suggest improvements to enhance development processes.