Director, AI - Software Engineering

Description Role: Director, AI – Software Engineering Location: North America - Remote Department: Exa Enterprise Support Group - EESG Reports to: CEO, Exa Capital Role Type: Player-Coach About Exa Capital Exa 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 Overview We 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 Execution • Define and execute AI roadmap at speed, aligned to enterprise priorities and each portfolio company's competitive context • Identify and prioritize high-impact AI use cases across: • Software development • Product innovation • Operational efficiency • Revenue enablement • Maintain a portfolio-wide AI backlog with clear ROI targets, success metrics, and prioritization frameworks • Redesign and operationalize an AI-powered Software Development Lifecycle across all stages • Continuously evaluate emerging technologies and make clear adopt / scale / defer decisions • Build and lead a lean, high-impact AI engineering team with strong hands-on capability • Develop and scale reusable playbooks, frameworks, and architecture patterns across teams • Strengthen internal capability to reduce reliance on external vendors and consultants • Drive adoption through structured training, change management, and AI champion networks Hands-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 companies Implement 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 risk End-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 impact • Evaluate AI and engineering maturity during acquisitions to inform investment decisions • Define standards for AI-powered development, coding agents, and engineering platforms • Accelerate post-acquisition integration through shared systems, playbooks, and reusable patterns Technical