Applied AI Lead

EQT is looking for an Applied AI Lead — a delivery-focused engineering leader who brings together technical depth, product instinct, and the credibility to partner with investment professionals, operators, and engineers to turn generative AI from prototype into production. This role sits at the heart of EQT’s ambition to become the most AI-literate firm in private markets.

You will lead EQT’s Applied AI function, a multidisciplinary team of applied AI engineers, ML engineers, and full-stack builders shipping high-impact generative AI products across Client, Investment Teams, and Fund/Group. The team consumes Tech Foundations’ shared AI platform while owning lightweight tooling and operational practices to move with speed.

You will lead a team of engineers deployed on high-impact projects to quickly drive results within both engineering and business teams. You will lead with craft, actively contributing to assignments alongside your leadership duties and you will act as a catalyst, helping engineering teams accelerate AI delivery and maximizing AI impact across business teams. You set the standard for quality and delivery across the team.

• Lead, mentor, and grow a multidisciplinary Applied AI team with clear expectations for delivery quality and technical craft.
• Own the Applied AI roadmap in close alignment with business stakeholders and value stream teams; stay attuned to emerging AI capabilities and evaluate where new models, agents, or tooling unlock fresh value.
• Partner with Tech Foundations, Product, Data, and Security teams to ensure Applied AI products integrate cleanly and responsibly.
• Serve as the senior AI partner to EQT’s business lines — working directly with investment professionals and leaders to translate opportunities into concrete deliverables.
• Drive adoption of shipped products through enablement, demonstrations, training, and active feedback loops with end users.
• Track the AI frontier and apply fresh thinking to raise the bar on what EQT can build.
• Measure and communicate business impact with rigour — instrumenting both engineering productivity gains and user adoption — and hire engineers who understand that judgment about AI output, decomposition skills, and willingness to change workflows matter more than framework credentials.

You are a thoughtful, delivery-oriented leader who combines technical depth with business fluency to navigate private markets. You bring collaborative leadership, sound judgment, and relentless curiosity about where AI creates genuine value in PE.

Leadership & Credibility: At least 10 years in software or ML/AI engineering, with 3–5 years in a leadership capacity managing teams or strategic delivery in applied AI, ML product, or AI platform environments.

Demonstrated AI Leverage & Outcomes: Concrete evidence of AI-driven productivity gains: a specific product, workflow, or internal tool you shipped with AI; a measurable before/after (latency, throughput, manual-work elimination); and how you measured it.

Willingness to Abandon Habits for Better Tooling: You’ve changed your mind on how you write AI systems because a new approach or model capability made the old one obsolete. You ship what works now, not what worked last year, and you bring that standard to how you lead.

Working Knowledge of Private Equity & Deal Workflow: Genuine operational fluency in PE — deal cycles, investment decision-making, portfolio workflows, or comparable high-stakes professional services. You translate vague “investment teams struggle with analysis” into shippable features and know where AI actually applies vs. distraction.

Measurement & Instrumentation: You cultivate a culture of transparency and clarity. You measure impact and productivity not just for dashboards, but to empower your team, identify where we win, and remove barriers to success. You believe that rigorous, data-informed insights create fairness and accountability—enabling you to hire, promote, and develop top-quartile talent who value ‘Owning It’.

Stakeholder & Cross-Functional Leadership: You’ve translated ambiguous business needs into shippable AI…