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
