Manager, AI Software Engineering

Description: Role: Manager, AI – Software Engineering

Location

North America – Remote (USA or Canada)

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 Manager 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 help redefine and implement 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

* Contribute to and execute the 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 recommend adopt / scale / defer decisions * Lead a small, 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
• Recommend scale / iterate / stop decisions based on measurable impact
• Support AI and engineering due diligence during acquisitions
• Apply and refine 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

• Implement 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
• Uphold and refine 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

Requirements: Required Experience

• Bachelor’s degree in Computer Science or related field; advanced degree preferred
• 6–8+ years of software engineering experience with recent hands-on experience
• 2+ years of engineering management experience leading individual contributors
• Hands-on experience with AI infrastructure and LLMs
• Experience building large-scale query processing or distributed systems
• Experience hiring and developing engineers
• 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

• Execution ownership of AI initiatives across multiple real businesses
• Direct influence with CEOs, CTOs, and investors
• Exposure to M&A and post-acquisition transformation
• Ability to help shape 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 within assigned team(s) * Coding agents and copilots adopted in real developer workflows * Measurable improvements in: + Engineering velocity + Code quality + Test coverage * 2–3 production-grade AI systems shipped in priority portfolio companies * 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 work alongside portfolio leadership to deliver AI outcomes
• Access to senior leadership on AI strategy 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