• Collaborate with Staff Engineers, Engineering, Product Management, and User Experience to define customer needs and use cases. • Develop and implement comprehensive unit, integration, and end-to-end tests to guarantee the reliability and performance in the upstream project, maintaining CI/CD workflows in GitHub, and ensuring downstream quality. • Create robust AI/ML software tools to enable AI Application development and contribute to a healthy open source community. • Participate in AI-assisted code reviews, utilizing tools that provide real-time feedback, identify potential bugs, security vulnerabilities, and adherence to coding standards, contributing to a more thorough and efficient review process. • Proactively utilize AI-assisted development tools (e.g., GitHub Copilot, Cursor, Claude Code) for code generation, auto-completion, and intelligent suggestions to accelerate development cycles and enhance code quality. • Create and maintain clear, concise upstream technical documentation including API references and user guides and collaborating with our internal tech writers to create robust downstream documentation. • Evaluate and integrate the latest advancements in AI/ML technologies and toolkits to improve existing systems and develop new innovative solutions.

• Collaborate with stakeholders (DT domain leads, DT experts and Laboratory Leads) to ensure application design and implementation plans are properly built and delivered on time. • Translate technical plans, designs, and specifications into clear, impactful language for users and management to support strategic decisions. • Review and challenge vendor proposals, designs, and implementation approaches • Facilitate the GxP compliant deployment of the solution • Oversee incoming and outgoing project documentation from a functional/technical point of view. • Understand the system and the relationships between sub-projects. Ensure coherence between LIMS, laboratory instruments, SAP/MES, reporting, and infrastructure components • Participate in the definition and implementation of testing approaches

• Lead the design and implementation of complex technology projects and strategic initiatives • Develop, test, and deploy high-quality, production-ready code for large-scale software components • Set the standard for code quality by performing rigorous peer reviews and establishing best practices • Solve sophisticated software problems involving high risk or complexity • Drive significant improvements in software quality, system performance, and the end-customer experience • Lead initiatives to improve testability, internal documentation, and operational excellence metrics • Communicate effectively with senior stakeholders and cross-functional partners • Proactively manage technical debt and improve runbooks and internal tooling • Actively mentor engineers and interns, serving as a technical guide to elevate the team’s overall capability.

• Architect and break down ambiguous product problems into concrete, well-scoped tasks for agents to execute and ship full-stack features, owning work from design through production release. • Design and operate complex agent orchestration systems that ship real product features. • Design APIs and data models that are clean, consistent, and built to last, not just fast to write. • Contribute to our AI-first engineering culture, share patterns, document what works, and help the team level up on how we build with AI. • Use AI tools fluently - Claude Code, Augment, or equivalent- to compress implementation time, then critically review everything that comes out of them. • Collaborate closely with Product and Design to identify gaps in specs, surface edge cases early, and push back when something isn't ready to ship. • Set the quality bar: well-structured, maintainable code; conduct thorough code reviews; maintain adequate test coverage; and catch bugs introduced by AI before they reach production.