EQT Group
AI Engineer Intern - EQT Digital - London
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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).
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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.
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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
Inclusion at EQT
Our vision for EQT employees is to build high performing & engaged teams. Our competitive edge comes from fostering an environment where every individual feels valued, empowered, and motivated to drive business impact. Our commitment to inclusion is not just about fairness; We understand and believe that being a great place to work drives the best performance.
At EQT, inclusion is a business imperative and it's embedded into our talent strategy, decision-making, and culture to ensure that every individual and team operates at their full potential. By doing so, we unlock better collaboration, stronger innovation, and superior investment outcomes.
About EQT
EQT is a purpose-driven global investment organization focused on active ownership strategies. With a Nordic heritage and a global mindset, EQT has a track record of over three decades of developing companies across multiple geographies, sectors and strategies. EQT has investment strategies covering all phases of a business’ development, from start-up to maturity. EQT has EUR 270 billion in total assets under management (EUR 141 billion in fee-generating assets under management), within two business segments – Private Capital and Real Assets.
With its roots in the Wallenberg family’s entrepreneurial mindset and philosophy of long-term ownership, EQT is guided by a set of strong values and a distinct corporate culture. EQT manages and advises funds and vehicles that invest across the world with the mission to future-proof companies, generate attractive returns and make a positive impact with everything EQT does. EQT has offices in more than 25 countries across Europe, Asia and the Americas and has more than 1,900 employees.
More info: www.eqtgroup.com
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