Master's Thesis Expert-in-the-Loop AgentOps/LLMOps Pipeline for Systems Engineering Process Agents

Master's Thesis Expert-in-the-Loop AgentOps/LLMOps Pipeline for Systems Engineering Process Agents

Working hours:
Full-time

Target Group:
Students

Department:
E-IO-SY

Published:
-

WE are...

At FEV.io GmbH, our mission is to revolutionize the automotive industry landscape and make intelligent mobility a reality for everyone. Through cutting-edge technology and software we aim to create a safer, greener and more connected vehicle experience for all.

We achieve this through our unparalleled, agile software engineering systems which deliver cutting-edge solutions to our clients including both forward-thinking automotive start-ups, as well as the world’s largest and most established automotive OEMs.

Agile and innovation are how we co about fulfilling our mission. Is this easy? Nope. In order to achieve this we have put together a team made some of the world’s brightest software minds. Backed by our parent company -FEV, with 6,100 talented people, we have both the know-how as well as experience to deliver on our promise.

Joining us will be joining a team which looks to the future to create software which is one step ahead.

In this thesis, an AgentOps/LLMOps pipeline will be designed and implemented to support engineering process agents with expert-in-the-loop feedback. The work addresses the challenge that current generative-AI agents in systems engineering (SE) — used for tasks such as requirements derivation, test-case generation, and subsystem decomposition — often produce inconsistent or untraceable outputs when deployed as black boxes.

The pipeline will embed domain experts into iterative feedback loops, capturing their corrections and context, and using that feedback to selectively adapt the system. The approach will operationalise both forward flow (methods/data → agent output → user feedback → iteration) and backward flow (user feedback → classification/context engineering → selective LLM adaptation/test-update), minimising unnecessary retraining and focusing on context engineering and selective adaptation.

The main tasks of the thesis include:

• Conducting a literature and state-of-the-art review on MLOps, LLMOps, and AgentOps, with focus on engineering process agents and expert-in-the-loop systems.
• Designing and implementing an open-source AgentOps/LLMOps pipeline tailored for SE use-cases.
• Empirically comparing adaptation strategies (context engineering, parameter-efficient fine-tuning, hybrid) on two SE tasks:
  (a) requirements derivation for subsystems,
  (b) test-case derivation from requirements.
• Measuring precision/recall, semantic correctness, expert corrections per iteration, and cost-benefit metrics.
• Quantifying the value of expert feedback in iterative agent improvement.

Your profile

• Solid programming skills, preferably in Python (experience with other languages such as C++/Java is a plus)
• Fundamental knowledge of machine learning / AI, ideally including large language models, prompt engineering, retrieval-augmented generation (RAG), and context engineering
• Basic understanding of software engineering concepts (APIs, data models, version control, testing) and cloud development (Azure preferred)
• Analytical skills to design experiments, measure performance metrics, and interpret results
• Ability to work independently, structure complex problems, and document results in scientific English
• Interest in operationalising AI agents and integrating expert feedback loops

Nice to have:

• Experience with context engineering, RAG, or fine-tuning LLMs.
• Interest in systems engineering processes and tools.

WE offer ...

Cutting-edge

Here being cutting-edge and innovative is more than a cool way to describe ourselves. It’s really what it’s like! You’ll be working with the best of the best including leading Automotive OEMs to create tomorrow’s software systems and solutions.

Flexibility

Flexible might be nice for some -but for us it’s a must! We want you to be relaxed, creative and efficient at your work, so we give you a work model that supports this! Want to choose your work-times? Sure! Want to work remote? Sounds like a plan!

Agility

Product prototypes within 2 day sprints? Yup! Scrums and agile workflows and communication? Also! We pride ourselves in having an atmosphere of agility, openness, trust and innovation - and practice it daily!

The Bottom line

Our atmosphere, flexibility and agility are great, but we also want to make sure you pass your studies with ease, so we offer the following:

• up to 100% remote work
• flexible working hours for your work-life balance
• FEV Academy and LinkedIn Learning
• Spendit Card - cashless benefit
• team events and parties
• modern lounges, electronically adjustable desks, flex desk, table football, Nintendo, coffee and tea