Director, AI - Software Engineering

Exa Capital Inc. is a permanent capital holding company focused on acquiring and building vertical market software businesses. They are seeking a Director of AI – Software Engineering who will define and execute AI strategy across a portfolio of companies, focusing on building production-grade AI systems to improve software development and operational efficiency.

Responsibilities:

• 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
• 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
• 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
• 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:

• 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
• 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