Senior Software Engineering Manager, AI Software Engineering Enablement

The Senior Manager, AI Software Engineering Enablement will lead FM?s transition to a governed, AI-enabled software delivery operating model.

This leaderis responsible fordefining, building, and scaling the enterprise capabilities that enable AI-assisted, traceable, secure, and compliant software delivery. The role oversees the adoption of modern engineering platforms, Enterprise Standards as Code, software development skills, shared services, governance automation, traceability services, release readiness, and AI-enabled development capabilities.

This leader must bring deep software engineering experience and extensive knowledge of modern engineering best practices, including software architecture, secure development, quality engineering, automated testing, code quality, maintainability, observability, delivery automation, and lifecycle governance. The leader will ensure that AI-enabled development strengthens, rather than bypasses, the engineering disciplines required to build reliable, secure, maintainable, and scalable software.

Working across Engineering, Architecture, Security, Audit, Compliance, and Platform Engineering, this leader willestablishthe frameworks, platforms, shared services, and governance capabilities that allow software teams to deliver consistently byleveragingAI while maintaining enterprise standards and regulatory expectations.

This is a manager-of-managers role with responsibility for teams focused on developer enablement, AI SDLC capabilities, shared services, engineering governance services, enterprise development standards, and AI software delivery platforms. The scope of impact spans software engineering efforts across FM, driving the adoption and evolution of AI-enabled software delivery capabilities throughout the enterprise.

The role is approximately 70% technical leadership and strategy and 30% organizational leadership, adoption, and stakeholder engagement.

Technical Leadership

• Own the executionand refinementof FM?s AI Software Delivery roadmap and target operating model.

• Establish and promote modern software engineering capabilities and best practices across FM, including:

• Software architecture and design

• Secure software development

• Code quality and maintainability

• Quality engineering and automated testing

• Observability and operational readiness

• CI/CD and delivery automation

• Developer productivity and experience

• Software lifecycle governance

• Establish enterprise AI software delivery capabilities, including:

• AI-Drivendevelopment

• Enterprise Standards as Code

• Development Skills

• Traceability Services

• Governance Services

• Release Readiness Services

• Enterprise Agent Platform capabilities

• Cost Management and FinOps

• AIDevOpscapabilities, including AI integration within engineering pipelines, testing, deployment, and operational workflows

• Ensure AI-enabled development enhances engineering rigor, quality, security, and maintainability rather than bypassing established software engineering disciplines.

• Drive enterprise adoption ofplatform andemerging AI-enabled software engineering capabilities.

• Define the strategy, lifecycle, and governance model for reusable software development skills, agents, standards, shared services, anddeliveryaccelerators.

• Establish metrics and measurement frameworks that quantify the value created by the AI SDLC, including productivity, quality, risk reduction, and return on investment for AI tooling and services.

• Establish architecture and operating models for enterprise agent execution, enterprise prompt and skills management, governance automation, and AI-enabled delivery workflows.

• Partner with Security, Architecture, Audit, Compliance, and Platform Engineering to embed governance directly into software delivery processes.

• Understand the value chain of applying generative AI to software development, including model selection, cost optimization, local versus cloud deployment strategies, toolchain orchestration, and performance management.

• Guidethe evolution and effectiveness of the AI SDLC across diverse technology stacks, including cloud-native, commercial off-the-shelf, and legacy application environments.

Product Ownership

• Drive the roadmap for enterprise AI software delivery and shared service capabilities.

• Prioritize investments across engineering standards, governance, traceability, developer experience, agent platforms, shared services, and enablement capabilities.

• Evaluate emerging technologies anddeterminetheir alignment with FM?s strategic software delivery direction.

• Ensure internally developed capabilitiesremainaligned with evolving enterprise platforms, software engineering best practices, and FM technology standards.

• Partner with software engineering leadership to drive adoption of new AI SDLC capabilities and operating model enhancements.

• Serve as the strategic owner for enterprise AI software engineering enablement capabilities, balancing innovation, engineering quality, governance, adoption, value realization, and long-term sustainability.

• 15+ years of experience in software engineering, software architecture, software delivery, platform engineering, DevOps, cloud engineering, developer experience, or related technical leadership disciplines.

• Deep software engineering experience designing, building, testing, deploying, and operating enterprise software solutions.

• 5+ years leading managers, senior engineers, architects, or technical leaders within enterprise technology organizations.

• Demonstratedexpertisein modern software engineering practices, including architecture, code quality, testing strategies, code review, secure software development, maintainability, observability, delivery automation, and engineering governance.

• Proven experience leading enterprise-scale engineering transformation, software delivery modernization, platform adoption, or developer productivity initiatives.

• Experience working across Engineering, Architecture, Security, Compliance, Audit, Platform Engineering, and Technology Leadership organizations.

• Experience defining enterprise engineering standards, governance frameworks, operating models, and cross-functional adoption strategies.

• Experience building or managing engineering platforms, shared services, developer ecosystems, or software delivery enablement programs.

• Experience managing technology investments, business value realization, vendor capabilities, and technical roadmap prioritization.

• Experience presenting strategy, roadmap progress, business value, andadoptionmetrics to senior leadership and executive stakeholders.

Technical Skills

• Modern software architecture, design patterns, development frameworks, and application lifecycle management.

• Software development best practices, including clean code, code review, maintainability, technical debt management, refactoring, and reusable design.

• Quality engineering practices, including test strategy, automated testing, continuous testing, performance testing, and quality automation.

• Secure software development, threat-informed engineering, vulnerability management, and security controls throughout the development lifecycle.

• Developer Experience practices, including specification-driven development, harness engineering, reusable development assets, and engineering enablement.

• AI agent frameworks, orchestration platforms, and multi-agent software engineering workflows.

• Policy-as-Code, Governance-as-Code, Standards-as-Code, and compliance automation.

• Platform engineering, shared services, and developer self-service architectures.

• Traceability, audit evidence, release readiness, and software delivery governance capabilities.

• Software engineering metrics, engineering intelligence, and value measurement frameworks.

• Enterprise architecture patterns and technology governance.

• Cloud platforms, distributed systems, APIs, integration patterns, and modern software delivery architectures.

• CI/CD,DevSecOps, release management, observability, and production readiness.

• FinOps principles and technology cost management practices related to cloud and AI services.

Preferred Experience

• Building enterprise developer platforms, shared engineering services, or internal engineering platforms.

• Leading GitHub Enterprise or large-scale engineering platform transformations.

• Deploying AI agents, Copilot extensions, enterprise skills, MCP-based integrations, or agent execution platforms.

• Establishing Standards-as-Code, Governance-as-Code, or Policy-as-Code capabilities.

• Building traceability, audit, compliance,releasegovernance, or evidence collection solutions.

• Leading enterprise transformation initiatives involving AI-assisted software engineering.

• Building, deploying, and operating software products in cloud-native environments.

• Modernizing legacy applications and introducing contemporary engineering practices across diverse technology stacks.

• Operating within highly regulated environments requiring governance, auditability, security, and compliance oversight.

• Defining AI engineering operating models, AI governance capabilities, or AI-enabled software delivery frameworks.

Leadership Competencies

• Deep software engineering judgment and credibility

• Strategic technology leadership

• Manager-of-managers leadership experience

• Enterprise transformation leadership

• Cross-functional influence and collaboration

• Strong executive communication and storytelling

• Governance, risk management, and compliance mindset

• Ability to balance innovation with engineering rigor and operational discipline

• Strong stakeholder management across technical and business functions

• Systems thinking and enterprise-wide perspective

• Ability to drive adoption and organizational change at scale

• Ability to develop strong engineering leaders and multidisciplinary technical teams

Success Measures

• Measurable adoption of approved AI-enabled software engineering and delivery capabilities across engineering teams.

• Successful enterprise rollout and adoption of GitHub Enterprise capabilities.

• Enterprise standardsbecomingconsumable by developers, AI assistants, agents, and delivery pipelines.

• Increased automation of software engineering, testing, governance, compliance validation, and audit evidence generation.

• Improved software quality, maintainability, security posture, operational readiness, release readiness, and traceability.

• Reduction in manual compliance and governance activities through reusableplatformand shared service capabilities.

• Increased developer productivity through approved skills, engineering standards, development kits, shared services, and AI-enabled delivery capabilities.

• Establishment of a sustainable enterprise operating model for governed AI-enabled software delivery.

• Demonstrated business value and measurable return on AI software delivery investments.

• Increased visibility into engineering quality, compliance, security, productivity, technical health, and software delivery performance across the technology organization.