Director, AI - Software Engineering

Job Description Description:Role: Director, AI – Software EngineeringLocation: North America - RemoteDepartment: Exa Enterprise Support Group - EESGReports to: CEO, Exa CapitalRole Type: Player-CoachAbout Exa CapitalExa Capital is a permanent capital holding company focused on acquiring and building vertical market software businesses. We take a long-term, stewardship-driven approach – buying and holding companies forever, and empowering leaders through a decentralized operating model.Position OverviewWe are seeking a Director of AI – Software Engineering who is fundamentally a strong software engineer first, AI leader second.This role is responsible for defining and executing AI strategy across a portfolio of companies, with a focus on building production-grade AI systems that materially improve software development, operational efficiency, and product competitiveness.You will work directly with CEOs, CTOs, and VP Engineering leaders, operating as a hands-on player-coach—earning trust through execution, not authority—and driving adoption of AI solutions that deliver clear business outcomes and measurable engineering impact.A core mandate of this role is to redefine the Software Development Lifecycle (SDLC) using AI, including building and deploying coding agents, developer copilots, and AI-powered automation systems with strong guardrails, governance, and reliability, especially in regulated enterprise environments.In this role, you will will be responsible for following areas:AI Strategy & Portfolio ExecutionDefine and execute AI roadmap at speed, aligned to enterprise priorities and each portfolio company's competitive contextIdentify and prioritize high-impact AI use cases across: Software developmentProduct innovationOperational efficiencyRevenue enablementMaintain a portfolio-wide AI backlog with clear ROI targets, success metrics, and prioritization frameworksRedesign and operationalize an AI-powered Software Development Lifecycle across all stagesContinuously evaluate emerging technologies and make clear adopt / scale / defer decisionsBuild and lead a lean, high-impact AI engineering team with strong hands-on capabilityDevelop and scale reusable playbooks, frameworks, and architecture patterns across teamsStrengthen internal capability to reduce reliance on external vendors and consultantsDrive adoption through structured training, change management, and AI champion networksHands-On Engineering Leadership· Operate as a hands-on player-coach, partnering directly with CTOs and engineering teams· Build trust through deep technical contribution and delivered outcomes, not authority· Embed within teams to unblock execution, accelerate delivery, and improve engineering effectiveness· Drive AI adoption with a clear focus on business outcomes (revenue, cost, efficiency) and engineering efficacy (velocity, quality, reliability)· Translate business priorities into executable engineering outcomes while standardizing best practices across companiesImplement AI Powered SDLC across portfolio companies· Drive adoption of modern AI-assisted development tools (coding copilots, prompt-driven workflows, automated testing and debugging)· Establish Human + AI collaborative development workflows across engineering teams· Improve engineering velocity through faster iteration cycles, automated documentation, and intelligent debugging· Architect and build AI coding agents for code generation, testing, code review, and workflow automation· Deliver AI-native developer experiences that materially improve productivity and engineering output· Design and enforce guardrails for AI-generated code including validation, security, compliance, and policy controls· Implement static and dynamic validation, security scanning, and vulnerability detection· Ensure compliance with data protection standards (PII, secrets management, data leakage prevention)· Define and enforce policy workflows, approvals, and governance controls· Implement human-in-the-loop systems for critical decision points and risk management· Ensure systems meet enterprise standards for reliability, auditability, and traceability· Build evaluation frameworks to measure code correctness, test coverage, performance, and regression riskEnd-to-End Delivery (Prototype ? Production) and M&A support· Own end-to-end delivery from prototype to production, ensuring real-world impact· Execute rapid 30–90 day cycles with production-grade outcomes· Build systems that are scalable, observable, and maintainable by design· Make clear scale / iterate / stop decisions based on measurable impactEvaluate AI and engineering maturity during acquisitions to inform investment decisionsDefine standards for AI-powered development, coding agents, and engineering platformsAccelerate post-acquisition integration through shared systems, playbooks, and reusable patternsTechnical Governance, Data Readiness & Responsible AI· Establish AI development standards, security protocols, and governance frameworks· applicable across diverse portfolio companies· Partner with IT and data teams to assess data readiness and enable responsible access and· integration for AI use cases· Guide build-vs-buy decisions for AI capabilities, evaluating third-party tools against custom· development with disciplined cost-benefit analysis· Establish and enforce responsible AI and data-handling guidelines, including clear governance· processes for approvals, risk review, and human-in-the-loop controls· Ensure AI implementations align with data privacy regulations, security requirements, and· compliance obligations· Maintain documentation to support audit and regulatory readinessTeam Building, Change Management & Capability Development· Build and lead a small, high-impact AI enablement team; coordinate with external specialists and vendors as needed· Drive adoption through structured change management, training, and communications alongside solution delivery· Build repeatable AI playbooks, frameworks, and documentation that enable portfolio company self-sufficiency over time· Develop talent assessment frameworks to help portfolio companies build and retain AI/ML capabilitiesRequirements:Required ExperienceAdvanced degree in Computer Science10+ years of software engineering experience with recent hands-on experience2+ years of engineering director experience, including managing managersDeep experience with AI infrastructure and LLMsExperience building large-scale query processing or distributed systemsStrong track record of recruiting and growing technical teamsExcellent collaboration and communication skills across global organizationsStrongly Preferred ExperienceExperience building coding agents or developer copilotsFamiliarity with: RAG (retrieval-augmented generation)Agent frameworksPrompt engineering and evaluationExperience in regulated industries (finance, healthcare, etc.)Experience in private equity, venture capital, or multi-company environmentsBackground in: Developer productivity platformsPlatform engineering or internal toolingExperience building AI centers of excellence or transformation programsWhat You'll Learn & GainOwnership of AI strategy across multiple real businessesDirect influence with CEOs, CTOs, and investorsExposure to M&A and post-acquisition transformationAbility to define next-generation AI-powered software developmentTangible, measurable impact on engineering and business outcomesWho You AreA hands-on builder who writes code and ships systemsEqually credible with engineers and executivesFocused on real outcomes, not experiments or hypeStrong in both system design and business impactPragmatic—balances speed with safety and qualityComfortable operating across multiple companies simultaneouslyA change leader who drives adoption through trust, clarity, and resultsWhat Success Looks Like (First 3–6 Months)AI-powered SDLC implemented across multiple teamsCoding agents and copilots adopted in real developer workflowsMeasurable improvements in: Engineering velocityCode qualityTest coverage3–5 production-grade AI systems deployed per companyDemonstrated ROI through: Cost reductionProductivity gainsRevenue impactWhy Exa· Permanent capital: build AI capabilities designed to last decades, not optimized for exits· Decentralized model: portfolio CEOs own outcomes—you act as a strategic force-multiplier, not a control layer· Direct access to the CEO on AI strategy, acquisitions, and portfolio priorities· The opportunity to shape what "great AI" looks like across an entire software portfolio· A culture of high standards, low ego, discipline, and intellectual honesty· Visible, tangible impact—your work will influence products, margins, and competitiveness in real time· A chance to help build a new kind of software holding company, with AI as a core advantage