AI Engineer Intern
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
About the Role
You will join EQT Digital to help build a structured, queryable data asset capturing technology, AI and digital project intelligence across EQT's portfolio. You will own the data pipelines that ingest unstructured data, parse and extract information, and design a data model that ensures the system is properly set up for users to consume across different interfaces. You will work closely with EQT Digital and broader EQT teams, apply prompt engineering techniques, and help stabilize pipelines for production use in Google Cloud.
Requirements
- Master's-level training in computer science, data science, machine learning or a related technical field, with practical AI engineering experience beyond coursework
- Solid Python skills and experience with cloud database and pipeline products
- Hands-on experience with LLM APIs and prompt engineering, including designing prompts for structured extraction and evaluating output quality
- Familiarity with modern AI tooling, LLM APIs and established libraries
- Experience designing data models to make data easy to query and expose
- Ability to work independently on a scoped problem, including technical development and interacting with domain experts
- Ability to communicate progress clearly to a non-engineering audience
- Comfort with the GCP ecosystem, BigQuery and Cloud Run experience useful
- Exposure to Terraform is a plus
- Familiarity with Airtable and orchestration tools such as Dust
Responsibilities
- Work with the EQT Digital and broader EQT teams to define the most valuable data assets and information to extract
- Design and implement document parsing pipelines to extract structured intelligence from unstructured sources, including text documents, slides, and internal correspondence
- Apply prompt engineering techniques to maximize accuracy and consistency across document types and formats
- Develop evaluation pipelines to monitor correctness and coverage of extracted information
- Define data model for structured storage in Google BigQuery
- Stabilize pipelines for production use in Google Cloud, including error handling, logging, alerting, and output quality checks
- Define consumption interfaces for the extracted data
- Document architecture decisions, data model choices and pipeline behaviour for handover, maintenance and extension
Benefits
- Visa Sponsorship available