Director, AI - Software Engineering

Job Description Job Description 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 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 readiness Team 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 capabilities Requirements: Required Experience Advanced degree in Computer Science 10+ years of software engineering experience with recent hands-on experience 2+ years of engineering director experience, including managing managers Deep experience with AI infrastructure and LLMs Experience building large-scale query processing or distributed systems Strong track record of recruiting and growing technical teams Excellent collaboration and communication skills across global organizations Strongly Preferred Experience Experience building coding agents or developer copilots Familiarity with: RAG (retrieval-augmented generation) Agent frameworks Prompt engineering and evaluation Experience in regulated industries (finance, healthcare, etc.) Experience in private equity, venture capital, or multi-company environments Background in: Developer productivity platforms Platform engineering or internal tooling Experience building AI centers of excellence or transformation programs What You’ll Learn & Gain Ownership of AI strategy across multiple real businesses Direct influence with CEOs, CTOs, and investors Exposure to M&A and post-acquisition transformation Ability to define next-generation AI-powered software development Tangible, measurable impact on engineering and business outcomes Who You Are A hands-on builder who writes code and ships systems Equally credible with engineers and executives Focused on real outcomes, not experiments or hype Strong in both system design and business impact Pragmatic—balances speed with safety and quality Comfortable operating across multiple companies simultaneously A change leader who drives adoption through trust, clarity, and results What Success Looks Like (First 3–6 Months) AI-powered SDLC implemented across multiple teams Coding agents and copilots adopted in real developer workflows Measurable improvements in: Engineering velocity Code quality Test coverage 3–5 production-grade AI systems deployed per company Demonstrated ROI through: Cost reduction Productivity gains Revenue impact Why 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