Director, AI - Software Engineering
hace 5 días
Plano
Job DescriptionDescription: 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 · 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 · 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, • 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, • 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, • 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, • 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 · 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