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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

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