Director, AI - Software Engineering
Required Skills
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
**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
Posted: 2026-04-29