AI Engineer Intern - EQT Digital - London

About The Team

EQT Digital is a specialist team of ~25 people globally embedded within EQT's Deal Services function, working across the full investment lifecycle to drive digital and AI-enabled value creation within EQT's portfolio. We focus on the larger companies in EQT's private equity and infrastructure buy-out funds, partnering closely with deal teams, portfolio company leadership and external advisors to translate technology capability into commercial outcomes. As a team, we operate at the intersection of data, technology and value creation, and we are building the internal infrastructure to make our collective knowledge compound over time.

This internship would be a direct contribution to that effort.

About The Role

EQT Digital has started defining a target architecture for its internal knowledge and intelligence layer: a structured, queryable data asset that captures technology, AI and digital project intelligence across EQT's portfolio. The goal is a data layer that every team member can access and continuously enrich, and that can be selectively surfaced to the broader EQT organisation and portfolio companies to generate insight from unique knowledge from our (mostly) unstructured data. As an AI Engineer Intern, you will own the data pipelines to ingest unstructured data, parse and extract information, and design a data model that ensures the system is appropriately set up for users to consume on different interfaces.

Concretely, you will: Work with the EQT Digital and broader EQT teams to define most valuable data assets / information to extract Design and implement document parsing pipelines to extract structured intelligence from unstructured sources, including text documents, slides, and internal correspondence (email, messaging platforms), applying prompt engineering techniques to maximize accuracy and consistency across document types and formats Develop evaluation pipelines to monitor correctness and coverage of the extracted information Define data model for structured storage in Google BigQuery Stabilize the 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 so the system can be handed over, maintained and extended About You You are a Master's student with hands-on AI engineering experience and a strong instinct for building things that solve users’ problems and can be productized. You are comfortable with ambiguity, ask questions before writing any code, and can reason about how solutions or features bring value to the team (in order to arbitrate prioritize requests).