Applied AI Lead

You will lead EQT's Applied AI function and a multidisciplinary team of applied AI engineers, ML engineers, and full-stack builders. You will own the Applied AI roadmap, partner with engineering, product, data, and security stakeholders, translate business opportunities into AI products, drive adoption through enablement and training, measure business impact, and contribute technically while developing the team and maintaining high standards for quality and delivery.

Responsibilities

• Lead and develop a multidisciplinary Applied AI team
• Own the Applied AI roadmap with business stakeholders and value stream teams
• Evaluate emerging AI models, agents, and tooling
• Partner with Tech Foundations, Product, Data, and Security teams
• Translate business opportunities into concrete AI deliverables
• Drive adoption through enablement, demonstrations, training, and feedback loops
• Track emerging AI capabilities and identify opportunities for value creation
• Measure and communicate engineering productivity gains and user adoption
• Contribute to technical assignments alongside leadership responsibilities
• Hire engineers with strong AI judgment, decomposition skills, and workflow adaptability

Requirements

• At least 10 years of experience in software or ML/AI engineering
• 3–5 years of experience in a leadership capacity managing teams or strategic delivery in applied AI, ML product, or AI platform environments
• Evidence of shipping an AI-driven product, workflow, or internal tool with measurable outcomes
• Working knowledge of private equity and deal workflows
• Experience measuring AI impact and productivity through instrumentation
• Experience translating ambiguous business needs into shippable AI products
• Experience leading across engineering, product, data, and security without formal authority
• Experience rolling out internal platforms or shared services and driving adoption
• Active and skeptical engagement with AI research and new capabilities
• Experience operating in large, complex organisations across time zones and shifting priorities
• Direct experience in private equity operations, investing, or portfolio company engagement
• Experience with data governance, security, and AI compliance frameworks
• Experience building developer experiences and internal tooling
• Experience enabling AI adoption at organisational scale
• Familiarity with AI infrastructure or platform team environments
• Public track record of open-source contributions, technical writing, or speaking on AI systems design or engineering practices

Benefits

• Paid time off
• Parental leave
• Wellbeing and wellness support
• Flexible working arrangements
• Learning and development opportunities