Master Thesis - Building Knowledge Graphs and Creating Queryable Data Warehouses Through LLM Methodology for Supply Chain Intelligence

Axis Communications AB; Lunds kommun; On-site; SEK 240,000 - 300,000; Part time

Job summary

Axis Communications AB invites two Master’s students to work on a thesis project focused on building knowledge graphs and creating queryable data warehouses through LLM methodology for supply chain intelligence. The project spans integrating diverse data sources and transforming them into a structured, queryable graph-based data model to empower intelligent questions against the data warehouse.

The work aims to compare a structured knowledge graph approach with improved schema documentation for

Qualifications

• Pursuing a Master’s degree in a technical field with strong fundamentals in data structures and software engineering.
• Experience with Python and SQL, comfortable writing complex queries and data transformation scripts.
• Experience with Git and GitHub for version control and collaborative development.
• Experience with knowledge graphs or graph databases (Neo4j, ontologies, RDF, property graphs).

Skills

Python

SQL

Git

Neo4j

Knowledge graphs

Education

Master program in Computer Science

Tools

GitHub

Job description

Master Thesis - Building Knowledge Graphs and Creating Queryable Data Warehouses Through LLM Methodology for Supply Chain Intelligence

Job Description

Category

Computer Science, Data Engineering, Data Science

Scope

Master Thesis 30 hp 2 students completing 30 credits each

Background

Axis Communications operates a complex global supply chain with multiple data sources, including ERP, CRM, financial, and manufacturing systems, logistics platforms, internal applications, etc. Currently, querying this distributed data requires deep technical knowledge of our data warehouse architecture, SQL expertise, and familiarity with our schemas. Business stakeholders with limited familiarity on data warehouse architecture may struggle to gain quick insights into critical questions that require data across different systems. Domain experts within our supply chain organization possess deep knowledge of our supplier ecosystem, distributor networks, and their complex relationships. However, this knowledge usually exists in documents, spreadsheets, and institutional memory—not completely in a structured, queryable format that can enhance our data systems.

Goal

• How do we capture this expert knowledge as a structured knowledge graph that enables an LLM to ask intelligent questions against our data warehouse?
• Can a properly constructed knowledge graph—enriched with supply chain domain semantics—outperform simpler approaches like improved schema documentation for enabling natural language querying and impact analysis?

Who are you?

For this thesis proposal, we are seeking two students with a strong technical foundation and genuine interest in the intersection of data engineering, knowledge representation, and AI-driven data access.

Ideal Profile

• You are studying a Master Program in Computer Science, Technical Physics, Mathematics, Industrial Engineering, or equivalent technical discipline
• You have strong fundamentals in data structures, algorithms, and software engineering principles
• You have experience with Python and SQL, you're comfortable writing complex queries and data transformation scripts
• You have experience with Git and GitHub for version control and collaborative development
• You have experience with knowledge graphs or graph databases (Neo4j, ontologies, RDF, property graphs)
• You are passionate about solving real-world data challenges and enjoy working with large, complex datasets
• You are highly motivated, work well in pairs and enjoy collaborative problem-solving
• You are eager to learn emerging technologies and can quickly adapt to new tools and frameworks

It’s a plus if you have:

• Familiarity with DBT (Data Build Tool) or similar data transformation frameworks
• Understanding of LLMs, prompt engineering, or retrieval-augmented generation (RAG)
• Basic understanding of supply chain concepts or domain-driven design principles
• Experience with API development (REST, GraphQL)

Type of Employment

Thesis Worker (Fixed Term)

Posting End Date

2026-11-15

Certain roles at Axis require background checks, which means applicable verifications will be done in these recruitments. Notice will be provided before we take any action.

About Axis Communications

We enable a smarter, safer world by creating innovative solutions for improving security and business performance. As a network technology company and industry leader, we offer solutions in video surveillance, access control, intercom, and audio systems, enhanced by intelligent analytics applications.

With around 6000 committed employees in over 50 countries, we collaborate with partners worldwide. Together, we thrive in our friendly, open, and collaborative culture and inspire each other to think beyond the expected. United by our commitment to inclusion, diversity, and sustainability, we consistently seek to develop our skills and way of working.

Let's create a smarter, safer world

For more information about Axis, please visit our website www.axis.com.

Listen to Get To Know Axis - Podcast

Karla Marie Paredes, [e-mail].