Sr Lead Software Engineer, Engineering Excellence

Salary: $61, per year

• Formal training or certification in software engineering concepts, plus 5+ years of hands‑on experience
• Proven ability to build and support secure, production‑ready applications with strong application health practices, including vulnerability remediation, dependency management, and technical debt reduction
• Practical experience with technical lifecycle management, including modernization of applications, remediation of outdated components, and early risk detection across software portfolios
• Demonstrated success using enterprise‑approved AI‑assisted development tools in the workplace, with the ability to set expectations for validating AI‑generated outputs for accuracy, performance, and security
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and adherence to resilience and security standards; experience coaching senior engineers and leads on compliant usage patterns and controls
• Proficiency in modern software engineering practices, including CI/CD automation, automated testing approaches, secure coding standards, and peer review processes
• Ability to explain complex technical concepts clearly to both technical and non‑technical stakeholders and influence decisions across cross‑functional teams
• Experience with agentic AI coding platforms such as Claude, Git Hub Copilot, or equivalent enterprise‑authorized tools in a production engineering environment, with the ability to govern their use responsibly at team or program scale
• Familiarity with application health frameworks, static analysis tools, software composition analysis, or similar vulnerability and lifecycle management platforms
• Experience helping define engineering standards, architectural patterns, or developer experience initiatives within a large‑scale technology organization
• Exposure to cloud‑native application development and infrastructure‑as‑code practices, with understanding of how platform choices affect long‑term maintainability
• Background in coaching or mentoring engineers on secure development practices, AI‑assisted workflows, or technical modernization programs

• Drive adoption and governance of enterprise‑approved AI‑assisted engineering practices, including agentic coding tools, to improve code quality, delivery speed, and operational results while promoting measurable validation standards and reuse of proven patterns within the SDLC toolchain
• Champion application health standards across the engineering organization and set the example for building new applications in a secure, resilient, and well‑architected state from the outset
• Lead technical lifecycle management efforts by identifying and addressing aging components, deprecated dependencies, and systemic vulnerabilities before they create risk or operational burden
• Partner with architecture and security teams to embed secure‑by‑design principles into development workflows and ensure applications meet firm‑wide health and compliance expectations throughout delivery
• Guide teams on responsible use of agentic AI coding tools, including clear expectations for human review, output validation, and secure handling of sensitive data in AI‑assisted workflows
• Help evolve engineering standards and toolchain automation by finding opportunities to reduce manual effort, speed up release readiness, and improve traceability and auditability across the delivery pipeline
• Mentor and coach engineers at different levels, sharing expertise in application health, AI‑enabled development, and technical lifecycle practices to strengthen team capability and promote engineering excellence
• Apply approved AI‑assisted engineering practices across teams to improve code quality,…