Senior Software Engineering Manager, AI Enablement

Salary: $111,000 - 151,000 per year Requirements:

• We need 15+ years of experience in software engineering, software architecture, software delivery, platform engineering, DevOps, cloud engineering, developer experience, or a related technical leadership field.
• We need deep hands-on experience designing, building, testing, deploying, and running enterprise software solutions.
• We need at least 5 years of experience leading managers, senior engineers, architects, or other technical leaders in enterprise technology organizations.
• We need strong command of modern engineering practices, including architecture, code quality, testing approaches, code review, secure development, maintainability, observability, delivery automation, and engineering governance.
• We need proven experience leading enterprise-scale engineering transformation, software delivery modernization, platform adoption, or developer productivity initiatives.
• We need experience working across Engineering, Architecture, Security, Compliance, Audit, Platform Engineering, and Technology Leadership groups.
• We need experience defining engineering standards, governance frameworks, operating models, and cross-functional adoption strategies.
• We need experience building or managing engineering platforms, shared services, developer ecosystems, or software delivery enablement programs.
• We need experience managing technology investments, business value realization, vendor capabilities, and technical roadmap prioritization.
• We need experience presenting strategy, roadmap progress, business value, and adoption metrics to senior leadership and executive stakeholders.
• We need practical knowledge of modern software architecture, design patterns, development frameworks, and application lifecycle management.
• We need familiarity with software development best practices, including clean code, code review, maintainability, technical debt management, refactoring, and reusable design.
• We need experience with quality engineering practices, including test strategy, automated testing, continuous testing, performance testing, and quality automation.
• We need experience with secure software development, threat-informed engineering, vulnerability management, and security controls throughout the development lifecycle.
• We need experience with developer experience practices, including specification-driven development, harness engineering, reusable development assets, and engineering enablement.
• We need knowledge of AI agent frameworks, orchestration platforms, and multi-agent software engineering workflows.
• We need familiarity with Policy-as-Code, Governance-as-Code, Standards-as-Code, and compliance automation.
• We need experience with platform engineering, shared services, and developer self-service architectures.
• We need understanding of traceability, audit evidence, release readiness, and software delivery governance capabilities.
• We need knowledge of software engineering metrics, engineering intelligence, and value measurement frameworks.
• We need familiarity with enterprise architecture patterns and technology governance.
• We need experience with cloud platforms, distributed systems, APIs, integration patterns, and modern software delivery architectures.
• We need working knowledge of CI/CD, DevSecOps, release management, observability, and production readiness.
• We need familiarity with FinOps principles and technology cost management practices related to cloud and AI services.
• Preferred experience includes building enterprise developer platforms, shared engineering services, or internal engineering platforms.
• Preferred experience includes leading GitHub Enterprise or large-scale engineering platform transformations.
• Preferred experience includes deploying AI agents, Copilot extensions, enterprise skills, MCP-based integrations, or agent execution platforms.
• Preferred experience includes establishing Standards-as-Code, Governance-as-Code, or Policy-as-Code capabilities.
• Preferred experience includes building traceability, audit, compliance, release governance, or evidence collection solutions.
• Preferred experience includes leading enterprise transformation initiatives involving AI-assisted software engineering.
• Preferred experience includes building, deploying, and operating software products in cloud-native environments.
• Preferred experience includes modernizing legacy applications and introducing current engineering practices across diverse technology stacks.
• Preferred experience includes operating in highly regulated environments requiring governance, auditability, security, and compliance oversight.
• Preferred experience includes defining AI engineering operating models, AI governance capabilities, or AI-enabled software delivery frameworks.
• We need deep software engineering judgment and credibility.
• We need strategic technology leadership.
• We need manager-of-managers leadership experience.
• We need enterprise transformation leadership.
• We need cross-functional influence and collaboration.
• We need strong executive communication and storytelling.
• We need a governance, risk management, and compliance mindset.
• We need the ability to balance innovation with engineering rigor and operational discipline.
• We need strong stakeholder management across technical and business functions.
• We need systems thinking and an enterprise-wide perspective.
• We need the ability to drive adoption and organizational change at scale.
• We need the ability to develop strong engineering leaders and multidisciplinary technical teams.

• We lead the execution and refinement of our AI software delivery roadmap and target operating model.
• We establish and promote modern software engineering capabilities and best practices across our organization, including architecture and design, secure development, code quality and maintainability, quality engineering and automated testing, observability and operational readiness, CI/CD and delivery automation, developer productivity and experience, and software lifecycle governance.
• We build enterprise AI software delivery capabilities, including AI-driven development, Enterprise Standards as Code, development skills, traceability services, governance services, release readiness services, enterprise agent platform capabilities, cost management and FinOps, and AIDevOps capabilities across engineering pipelines, testing, deployment, and operational workflows.
• We ensure AI-enabled development strengthens engineering rigor, quality, security, and maintainability rather than bypassing established software engineering disciplines.
• We drive enterprise adoption of platform and emerging AI-enabled software engineering capabilities.
• We define the strategy, lifecycle, and governance model for reusable software development skills, agents, standards, shared services, and delivery accelerators.
• We 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.
• We establish architecture and operating models for enterprise agent execution, enterprise prompt and skills management, governance automation, and AI-enabled delivery workflows.
• We partner with Security, Architecture, Audit, Compliance, and Platform Engineering to embed governance directly into software delivery processes.
• We assess 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.
• We guide the evolution and effectiveness of the AI SDLC across cloud-native, commercial off-the-shelf, and legacy application environments.
• We drive the roadmap for enterprise AI software delivery and shared service capabilities.
• We prioritize investments across engineering standards, governance, traceability, developer experience, agent platforms, shared services, and enablement capabilities.
• We evaluate emerging technologies and determine how well they align with our strategic software delivery direction.
• We make sure internally developed capabilities stay aligned with evolving enterprise platforms, software engineering best practices, and our technology standards.
• We partner with software engineering leadership to drive adoption of new AI SDLC capabilities and operating model enhancements.
• We serve as the strategic owner for enterprise AI software engineering enablement capabilities, balancing innovation, engineering quality, governance, adoption, value realization, and long-term sustainability.

• AI
• AI Agents
• CI/CD
• Cloud
• Copilot
• DevSecOps
• DevOps
• GitHub
• MCP
• Security

More:

Established nearly two centuries ago, we are FM, a leading mutual insurance company whose capital, scientific research capability, and engineering expertise are dedicated to property risk management and the resilience of our policyholder-owners. Our owners include many of the worlds largest organizations, including one of every four Fortune 500 companies, and we work with them to understand the hazards that can affect business continuity so they can make cost-effective risk management decisions that combine property loss prevention with insurance protection. This Senior Manager, AI Software Engineering Enablement role leads our move to a governed, AI-enabled software delivery model and has broad impact across Engineering, Architecture, Security, Audit, Compliance, and Platform Engineering. The position is approximately 70% technical leadership and strategy and 30% organizational leadership, adoption, and stakeholder engagement. We offer a comprehensive Total Rewards program that includes an incentive plan, health and well-being programs, a 401(k) and pension plan, career development opportunities, tuition reimbursement, flexible work, time off allowances, and more. We are committed to attracting, developing, and retaining a diverse workforce.

last updated 35 week of 2026