Artificial Intelligence Engineer

We back ambitious management teams of well-positioned mid-sized companies and help them grow profitably, organically and through acquisitions. This role turns data and models into sharper investment judgment and faster operational improvement. You will sit close to deal teams and portfolio leaders, so the systems you build change real decisions—not slide decks.

Performance Objectives

• Stand up a repeatable AI workflow that screens inbound and sourced opportunities and flags strategic and operational upside within days, not weeks.
• Build diligence tools that synthesize financial, commercial, and operational evidence into clear findings investment partners can challenge and use.
• Deliver portfolio analytics that track profitability drivers and growth levers, and surface actions management teams can act on each quarter.
• Design acquisition-support models that compare targets, integration risks, and value-creation cases on a consistent basis.
• Embed ESG signals into investment and ownership workflows so material risks and improvement opportunities are visible before and after close.
• Set data standards, model documentation, and human review so outputs are auditable, explainable, and trusted by investment professionals.
• Prove impact: shorter cycle times, higher signal-to-noise in memos, and at least one adopted use case per major investment process within the first year.

Environment & Resources

You will work in a small, senior team and report to investment leadership, with direct access to deal professionals and portfolio management. Scope includes internal data, permitted external sources, and practical tooling chosen for reliability over novelty. Budget is tied to use cases that improve decisions or portfolio performance. You will coach colleagues; you will not own a large engineering department.

Essential Qualifications

• Shipped applied AI or machine-learning systems used by non-technical decision-makers, with clear measures of adoption and accuracy.
• Strong Python and data engineering fundamentals: pipelines, evaluation, versioning, and secure handling of confidential information.
• Ability to translate messy commercial and financial questions into models, retrieval systems, or structured analyses that a partner can defend.
• Judgment on when not to automate, including limits of generative models in valuation, forecasting, and sensitive ESG claims.
• Experience partnering with operators or investors in finance, industrials, or business services is an advantage, not a substitute for delivery.

Role Selling Points

• Work on live ownership, not a sandbox: your tools support strategy, capital, and hands-on improvement of real businesses.
• Breadth across sourcing, diligence, value creation, and add-on acquisitions.
• Long-term partnership with entrepreneurs and capable management teams, not a trading-desk cadence.
• Room to set the firm’s AI standard early, including sustainability integrated through the investment cycle.

Call to Action

If you want your models judged by better decisions and stronger businesses, apply with two examples of systems you built, who used them, and what changed.