Director, AI - Software Engineering

Description

Position:
Director, AI – Software Engineering

Location:

North America - Remote

Department:
Exa Enterprise Support Group - EESG

Reports to:

CEO, Exa Capital

Role Type:
Player‑Coach

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.

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.

• 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

• 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

• 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