Applied AI Lead
EQT Ventures is the early-stage venture capital strategy of EQT Group, partnering with founders building 'Generation-Defining Companies.' With 1.1 billion euro invested behind the strategy, EQT Ventures acts as an early-stage lead investor, operational advisor, and high-conviction capital provider for ambitious European founders, typically investing between 2-50M EUR.
About EQT Ventures
EQT Ventures partners with founders building Generation-Defining Companies, aiming to back ambitious visions that can become game-changing, resilient businesses. As early-stage lead investors, operational advisors, and high-conviction capital providers, the team—made up of ex-founders and operators—provides hands-on support to help portfolio companies scale fast, break through barriers with access to the global EQT platform, and build long-term resilience. EQT Ventures is domiciled in Luxembourg, with investment advisors located in Stockholm, Paris, London, New York, Berlin and Amsterdam, and is focused on European companies, having backed over 140 founding teams to date across sectors such as AI, fintech, edtech, robotics, and aerospace. The firm operates through multiple funds, including EQT Ventures I, II, and III, with clients/portfolio companies ranging from early-stage startups like Sana Labs, 1X, Payrails, Parloa, and The Exploration Company.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
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.
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
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
Benefits
- Paid time off
- Parental leave
- Wellbeing and wellness support
- Flexible working arrangements
- Learning and development opportunities
