Director, AI - Software Engineering

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

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