Thesis Work, 30 Credits - Knowledge-Graph Based Maintenance Agent in Pharmaceutical Manufacturing

AstraZeneca; Södertälje kommun; On-site; SEK 201,000 - 290,000; Full time

Job summary

AstraZeneca in Södertälje is offering a thesis worker position focused on building and evaluating a knowledge-graph-based maintenance knowledge base for an LLM-powered agent in pharmaceutical manufacturing. The project involves transforming maintenance records into structured data, extending an information-extraction pipeline, and validating the agent against real-world scenarios.

You will collaborate with operators, maintenance personnel and technical experts to deliver a functional agent and

Qualifications

• Enrolled in a master’s programme in industrial engineering, computer science, artificial intelligence, production systems or a related field.
• Strong interest in LLM‑Ops, agent development and applied artificial intelligence.
• Strong communication and collaboration skills, including ability to interact with operators, engineers and subject‑matter experts.

Responsibilities

• Transform historical maintenance knowledge into a knowledge-graph-based maintenance knowledge base.
• Evaluate an agent powered by a large language model (LLM) for maintenance questions.
• Work with real manufacturing data in a regulated environment and collaborate with operators and technical experts.

Skills

LLM-Ops

Agent development

Applied AI

Education

Master's program in industrial engineering

Master's program in computer science

Master's program in artificial intelligence

Master's program in production systems

Tools

Knowledge graphs

Information retrieval

Job description

Are you interested in AI and pharmaceutical manufacturing? In this thesis project, you will develop and evaluate a knowledge-graph-based maintenance agent using real-world maintenance data.

About AstraZeneca

AstraZeneca is a global, science-led, patient-centred biopharmaceutical company focusing on discovering, developing, and commercialising prescription medicines for some of the world’s most serious diseases. But we’re more than a global leading pharmaceutical company. At AstraZeneca, we’re dedicated to being a Great Place to Work and empowering employees to push the boundaries of science and fuel their entrepreneurial spirit.

About The Opportunity

As a Thesis Worker at AstraZeneca, you’ll find an environment that’s full of unique opportunities and exciting challenges. Here, you’ll have the opportunity to pursue your areas of interest whilst equally developing a broad skillset and knowledge base to get the best out of your experience. You’ll be working on meaningful projects to make an impact and deliver real value for our patients and our business.

Thesis Work Description

Sustaining high uptime in pharmaceutical production depends on how quickly the right knowledge reaches the person standing at the machine. This knowledge comes from two complementary sources: the data generated by production and the accumulated understanding of how equipment behaves and how faults have been resolved in the past. A reliable agentic system needs access to both.

In this thesis project, you will focus on the second source: transforming historical maintenance knowledge into a knowledge-graph-based maintenance knowledge base that can be queried by an agent powered by a large language model (LLM). The data consists of historical maintenance records written as free text by technicians who carried out the work.

The project builds on an existing information-extraction pipeline and agent prototype. You will investigate how maintenance records can best be transformed into a useful and scalable knowledge graph, and evaluate how effectively the LLM-driven agent can retrieve and use this knowledge when answering realistic maintenance questions.

Key Objectives

• Verify the current knowledge graph construction pipeline and improve its underlying ontology against real maintenance records.
• Investigate strategies for building an inductive knowledge graph, one that takes in new work orders, equipment, and fault types without being rebuilt.
• Gather realistic maintenance questions from engineers and technicians and build them into an expert-reviewed evaluation set.
• You will work with real manufacturing data in a regulated environment and collaborate closely with operators, maintenance personnel and technical experts. The project combines practical work in agentic AI and data engineering with knowledge representation and evaluation in an industrial setting.

Outcome

You will gain hands‑on experience with agentic AI built on real manufacturing data in a regulated environment and will deliver a working agent together with an evidence-based assessment of its reliability.

Placement

This is an on-site position at AstraZeneca Södertälje.

Please note, AstraZeneca does not support with accommodations for this role.

Structure

• Duration: Spring 2027
• Credits: 30
• You will work together with another master’s thesis student who has already been identified for the project.

Essential Requirements

• Enrolled in a master’s programme in industrial engineering, computer science, artificial intelligence, production systems or a related field.
• Strong interest in LLM‑Ops, agent development and applied artificial intelligence.
• Strong communication and collaboration skills, including the ability to interact with operators, engineers and subject‑matter experts.
• Experience with knowledge graphs or information retrieval is an advantage but not a requirement.

Date Posted

22-sep.-2026

Closing Date

13-okt.-2026

Our mission is to build an inclusive and equitable environment. We want people to feel they belong at AstraZeneca and Alexion, starting with our recruitment process. We welcome and consider applications from all qualified candidates, regardless of characteristics. We offer reasonable adjustments/accommodations to help all candidates to perform at their best. If you have a need for any adjustments/accommodations, please complete the section in the application form.