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).

What you'll bring

• Master's-level training in computer science, data science, machine learning or a related technical field (you can be in the process of completing your Master), 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: you know how to design prompts for structured extraction, how to evaluate output quality, and how to iterate when results are inconsistent
• 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 to make informed decisions
• Ability to communicate progress clearly to a non-engineering audience

Useful but not required

• Comfort with the GCP ecosystem — BigQuery and Cloud Run experience is useful
• Exposure to Terraform is a plus
• Familiarity with Airtable as a lightweight data layer alongside more structured warehousing approaches; and with orchestration tools such as Dust
• Interest in private equity or investment management contexts: you don't need a finance background, but intellectual curiosity about what we do is important

What Success Looks Like

We aim to work in sprints, defining which data sources are priority to ingest as part of our knowledge base  and then working from raw ingestion through to a working consumption interface, with a data model designed, pipeline automated, and output queryable by the team. A successful internship means several of those data sources are ingested in the knowledge base and in active use by the team before you leave. Concretely:

• Each data source you take on is fully operational end-to-end: ingested, parsed, structured in BigQuery, automated on GCP, and accessible via at least one interface. Extraction quality is measurable and documented: the team can see what confidence looks like and trust the output
• Each iteration is documented as you go 

The order of data sources and the scope of each iteration will be agreed at the start and re-evaluated regularly. We stay agile: if an earlier source surfaces unexpected complexity, we adapt rather than push through

What We Offer

• Ownership of a meaningful technical problem
• Direct collaboration with a small, senior team with diverse profiles (all coming from Tech backgrounds & companies)
• Exposure to how a global PE firm thinks about data, AI and technology across a portfolio of approximately 200 companies
• Opportunity to see your work move into production and be used by the team before your internship ends

Logistics

Duration: 6 months (flexible depending on availability and academic calendar)

Location: London 

Start date: September or October 2026, Visa Sponsorship available