• Manage, mentor, and develop a team of software engineers across all experience levels • Conduct regular 1:1s, provide timely and constructive feedback, and support each engineer’s professional growth and career development • Partner with recruiting to grow the team by attracting and hiring engineers aligned with the team’s AI-native technical direction • Set clear expectations, foster accountability, and build a culture of trust, collaboration, and continuous improvement • Address performance issues constructively and proactively • Own the team’s delivery commitments; ensure features, bug fixes, and technical investments are completed on time and meet quality standards • Participate in sprint planning, backlog refinement, and prioritization discussions with Product and stakeholders • Remove blockers, manage dependencies, and ensure the team has what it needs to execute effectively • Monitor and continuously improve team velocity, predictability, and delivery health within the agentic pod model • Maintain strong technical engagement: participate in architecture discussions, code reviews, and technical planning • Ensure the team adheres to engineering standards for C# / .NET, API design, database patterns, front-end development, and CI/CD using Azure DevOps • Identify and address technical debt; champion sustainable engineering practices • Drive adoption and effective use of AI-assisted development tools (GitHub Copilot, Claude, Azure OpenAI) across the team; ensure engineers are working in an agentic delivery rhythm where AI handles generation at scale and engineers own judgment, quality, and architectural decisions • Partner with other engineering leaders to define and uphold organizational AI development standards, prompt engineering practices, and AI output review requirements • Track and evaluate team-level engineering metrics, including sprint velocity and predictability, CI/CD pipeline health, production incidents, defect backlog trends, and system performance data to identify systemic issues and drive continuous improvement • Use data to communicate engineering health, technical decisions, tradeoffs, and statuses clearly to stakeholders and leadership • Contribute to engineering-wide initiatives including hiring practices, standards, tooling, and process improvement

• Design and implement full-stack features using C# / ASP.NET Core, Entity Framework Core, and SQL Server / Azure SQL for back-end services, and React or Angular for front-end applications • Write well-tested, maintainable code with unit and integration tests • Participate actively in code reviews, providing and incorporating constructive feedback • Contribute to Azure DevOps CI/CD pipelines and deployment workflows • Troubleshoot and resolve production issues in a timely manner • Use AI-assisted development tools (GitHub Copilot, Claude) with structured, context-rich prompts to accelerate coding, test coverage, and documentation • Critically evaluate all AI-generated output before committing — review for correctness, security risks, and alignment with team architecture and coding standards • Work within Libra’s agentic engineering model: AI agents handle code generation at scale; engineers own the judgment, quality bar, and architectural decisions on everything that ships • Collaborate with Product, QE, business stakeholders, and peers to deliver features that meet acceptance criteria and quality standards • Partner with offshore developers on the squad to scope tasks clearly, provide code review feedback, and maintain shared quality standards

• Contribute to the development of our web, mobile, and backend applications. • Partner with Product and Design to guide the direction of our products, integrations, and technology. • Integrate third-party technologies including medical devices and EHRs into our platform. • Use cutting-edge generative AI techniques to build intelligence into our clinical platform.

• Act as a team advisor and technical thought leader, guiding direction and execution of high-impact initiatives within the domain. • Own complex production issues end-to-end, from triage through resolution, driving sustainable fixes and prevention strategies. • Lead root-cause analysis (RCA) across application, API, and database layers. • Design and implement maintainable, scalable, and testable solutions for defect resolution and system improvements. • Originate and institutionalize best practices, standards, and methodologies that improve engineering effectiveness and platform reliability. • Balance short-term fixes with long-term architectural integrity and maintainability. • Solve highly complex, cross-domain problems using advanced technical knowledge and broad experience. • Diagnose and optimize system performance across frontend, backend, and database layers. • Analyze Oracle database performance, including SQL optimization and execution plan tuning. • Lead technical debt reduction initiatives, prioritizing work that delivers meaningful improvements to system health and developer productivity. • Introduce new tools, approaches, or patterns that elevate operational efficiency and platform resilience. • Develop and maintain solutions in TypeScript/React and C#/.NET Core services. • Write and maintain automated unit and integration tests to reduce defects and prevent regression. • Refactor legacy code to improve clarity, maintainability, and performance. • Ensure adherence to secure coding practices and enterprise standards. • Build strong relationships and influence stakeholders across engineering, product, and operations through clear communication of complex concepts. • Translate deep technical issues into actionable insights for cross-functional partners. • Provide mentorship and guidance to engineers, actively developing team capabilities and avoiding common pitfalls. • Contribute to a culture of continuous improvement, intellectual humility, and engineering excellence. • Own incoming operational requests during business hours and drive timely resolution. • Create and maintain runbooks, documentation, and training materials to improve response consistency and reduce operational friction. • Collaborate closely with Operations, Product, and other stakeholders to ensure issues are properly prioritized and understood within business context. • Utilize observability tools (e.g., Application Insights, Datadog) to proactively monitor and improve system performance. • Leverage AI-assisted tools and agents to accelerate triage, debugging, and root-cause analysis workflows. • Design, build, and maintain AI-enabled developer tooling (e.g., MCP servers, diagnostics automation) to improve team productivity. • Establish responsible AI usage practices, ensuring outputs are validated, explainable, and aligned with engineering standards.