Master Thesis

DescriptionDescriptionAt ELCA, we support and maintain a wide range of enterprise solutions based on Microsoft Dynamics 365. These solutions involve custom developments using C#, JavaScript and more, tracked through Azure DevOps (ADO) using work items (User Stories, Tasks, Bugs) and source code repositories (Git). During maintenance phase, customer reports issues (bugs and support need) by submitting tickets via ADO (Azure DevOps).Currently, our consultants manually analyze these tickets, make sure they have enough context to reproduce the case and base their analysis with historical knowledge, then inspect related code to diagnose and resolve bugs. This approach is time-consuming, potentially requires back-and-forth with customers and depends heavily on human expertise and familiarity with past project history.We are in the AI era; in this internship we propose to leverage machine learning and language models to enhance software maintenance and support. By building an AI-powered troubleshooting agent that has access to the code and documentation and is able to learn from past project history, bug reports, and associated code changes, we can drastically reduce resolution times, improve accuracy, and empower support teams with contextual suggestions. This AI solution will not only streamline operations but also contribute to building a dynamic and self-improving knowledge base over time.As an intern, you will join a team of experts and passionate professionals working at the crossroads of enterprise IT and artificial intelligence. You will evolve in a stimulating, collaborative environment that encourages innovation, initiative, and technical excellence. You'll be supported by experienced mentors, have access to modern tools and infrastructure, and contribute to a project with real impact for customers and internal teams alike.ObjectivesThe objective of this internship is to design and build an AI-based troubleshooting assistant that will:Ingest past ADO tickets and associated source code to build a searchable knowledge base. But also Dynamics 365 docs to help. Automatically analyze new bug tickets using AI / NLP and suggest potential root causes and code fixes, based on prior patterns and source code.Offer a user-friendly UI for support specialists to interact with the AI assistant, upload context, and receive suggestions or proposed fixes.Phase 1 – AI Knowledge Base & Suggestion Engine• Crawl and index historical ADO tickets• Link ADO tickets to code segments• Use NLP techniques and vector-based semantic search• Optionally: Integrate code understanding via models like Qwen2/Codellama or other to suggest code changesBonus: Phase 2 – Frontend / UX Integration• Build a lightweight web-based UI in the language of your choice.• Authentication + access to user-specific ADO projects via Microsoft Graph API or Azure DevOps API.Our offerA dynamic work and collaborative environment with a highly motivated multi-cultural and international sites teamThe chance to make a difference in peoples' life by building innovative solutionsVarious internal coding events (Hackathon, Brownbags), see our technical blogMonthly After-Works organized per locationsSkills requiredMust-Have:• Strong skills on AI & NLP/ML.• Programming: Strong skills on object-oriented programming, ideally C#. And skills with Java Script.• Experience with LLMs (OpenAI, LLama, Qwen2, etc.)• DevOps concepts.• Databases: SQL or NoSQL• Version Control: GitNice-to-Have:• Knowing Azure DevOps (ADO).• Vector search tools (e.g., FAISS, Azure AI Search, Pinecone)• Web Dev: , Blazor, or ASP.NET Core• Azure services: Functions, Cognitive Search, Azure OpenAI• Familiarity with Dynamics 365 project structures.This internship starts in February 2026. Applications must include your most recent academic transcripts (grades); applications without transcripts will not be considered.