Working Student (m/f/d) Applied AI Engineering & Prototyping

📍 Job Overview

Job Title: Working Student (m/f/d) Applied AI Engineering & Prototyping

Company: Körber Group

Location: Berlin, Germany

Job Type: Intern

Category: Applied AI Engineering & Prototyping

Date Posted: October 1, 2026

Experience Level: Entry Level (0-2 years)

Remote Status: On-site

🚀 Role Summary

• This role focuses on the hands-on exploration and technical feasibility of AI applications, with a strong emphasis on rapid prototyping and experimental development.

• You will be instrumental in testing technical possibilities, building tangible demos, and transforming promising AI concepts into working solutions.

• The position is closely aligned with an AI Engineer role, requiring an experimental, product-oriented, and hands-on approach to applied AI topics.

• Key areas of focus include Large Language Models (LLMs), agentic systems, API integrations, lightweight frontends, and enterprise system connectivity.

📝 Enhancement Note: This role is described as a "Working Student," indicating it's a part-time position designed for individuals currently enrolled in a university program, offering practical experience in a specialized technical field. The emphasis on prototyping and experimental development suggests a dynamic, fast-paced environment where learning and iterative development are paramount.

📈 Primary Responsibilities

• Build and iterate on AI prototypes, demos, and proof-of-concept applications to showcase technical capabilities and potential.

• Experiment with LLM-based applications, including prompt engineering, agentic workflows, and orchestration patterns to drive innovation.

• Connect prototypes to APIs, data sources, and enterprise systems to validate integration feasibility and functional scope.

• Support the development of lightweight frontend or app prototypes, making AI concepts tangible and testable for stakeholders.

• Conduct structured evaluations and benchmarks for AI models, tools, and technical approaches to inform decision-making.

• Assist with data preparation, prompt engineering, integration logic, and workflow design to support prototype development.

• Document prototype architectures, key technical learnings, and implementation trade-offs to ensure knowledge transfer and future reference.

📝 Enhancement Note: The responsibilities highlight a blend of technical development and analytical evaluation. The inclusion of "connecting prototypes to APIs, data sources, and enterprise systems" implies that this role will bridge the gap between theoretical AI concepts and practical business applications within the Körber Group's ecosystem.

🎓 Skills & Qualifications

Education: Currently enrolled in a university program such as Computer Science, Data Science, AI Engineering, Software Engineering, Business Informatics, or a closely related technical field. A Bachelor's degree is the typical academic foundation for this level.

Experience: Prior hands-on programming experience, ideally within a university project or internship setting. Familiarity with Git-based development workflows is essential for collaborative coding.

Required Skills:

• Hands-on programming experience with Python.

• Proficiency with Git-based development workflows.

• Strong curiosity and foundational understanding of modern AI development topics, including LLMs, retrieval-augmented generation (RAG), agentic systems, and applied prototyping.

• Ability to work in a structured manner and communicate technical findings clearly.

• Comfort navigating ambiguity and iterating towards workable solutions.

• Fluency in English. Preferred Skills:

• First experience with LLM APIs (e.g., Azure OpenAI).

• Familiarity with AI orchestration frameworks such as LangChain or Microsoft Agent Framework.

• Experience with RAG patterns and vector stores (e.g., pgvector).

• Exposure to evaluation tooling like Mlflow.

• German language proficiency.

📝 Enhancement Note: The emphasis on "first hands-on programming experience" and "first experience with LLM APIs" suggests that this role is geared towards students with foundational technical skills who are eager to gain practical, specialized experience in applied AI. The preferred skills point towards specific technologies and frameworks that would give candidates a significant advantage.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase of personal or academic projects demonstrating practical application of programming skills, particularly with Python.

• Examples of contributions to Git repositories, illustrating collaborative development or version control proficiency.

• Demonstrations of AI concepts, even if through theoretical frameworks or small-scale experiments, highlighting an understanding of LLMs or related AI fields.

• Documentation of technical problem-solving approaches, emphasizing structured thinking and clear communication of findings. Process Documentation:

• Evidence of documenting technical learnings, project architectures, or implementation trade-offs in previous projects.

• Examples of structured evaluation or benchmarking exercises, even if within an academic context.

• Clear articulation of iterative development processes, showcasing how ideas were tested and refined.

📝 Enhancement Note: For a "Working Student" role focused on prototyping and applied AI, a formal portfolio might not be as extensive as for a full-time engineer. The emphasis will likely be on demonstrating foundational programming skills, a strong interest in AI, and the ability to learn and document technical processes. Projects showcasing experimentation and problem-solving will be highly valued.

💵 Compensation & Benefits

Salary Range: As a "Working Student" position in Berlin, Germany, typical compensation would range from €12 to €18 per hour, depending on the specific year of study and relevant experience. This translates to an approximate monthly gross salary of €960 to €1,440 for a standard 20-hour work week.

Benefits:

• Practical, hands-on experience in cutting-edge Applied AI Engineering and Prototyping.

• Opportunity to work with advanced AI technologies and tools like LLMs, agentic systems, and RAG.

• Mentorship and guidance from experienced AI Engineers and technical leaders.

• Exposure to real-world business challenges and the opportunity to contribute to tangible solutions.

• Networking opportunities within a global technology group (Körber Group).

• Potential for future career development and full-time opportunities within the company.

Working Hours: This is a part-time role, typically around 15-20 hours per week, with flexibility to accommodate university study schedules. The total weekly hours are approximately 20.

📝 Enhancement Note: Salary estimates are based on common practices for working student positions in Germany, specifically Berlin, for roles requiring technical skills. The benefits focus on the learning and development aspects crucial for student roles.

🎯 Team & Company Context

🏢 Company Culture

Industry: The Körber Group operates across diverse, high-tech industries including Supply Chain, Pharma, and Industrial Automation. This provides a broad context for applied AI, with potential applications in optimizing logistics, manufacturing processes, and scientific research.

Company Size: The Körber Group is a large, global technology company with over 11,000 employees. This scale offers stability, extensive resources, and a wide array of potential projects and internal mobility.

Founded: Körber was founded in 1945. This long history suggests a stable and established organization with a deep understanding of industrial and technological evolution.

Team Structure:

• The AI Engineering & Prototyping team likely consists of specialized engineers and researchers focused on exploring and developing new AI solutions.

• Reporting structure will likely be within a technology or innovation department, potentially reporting to an AI Lead or Head of Innovation.

• Cross-functional collaboration is expected, working with product teams, R&D, and potentially business units to define and test AI use cases. Methodology:

• Data-driven decision-making, with a focus on empirical evidence from prototyping and benchmarking.

• Agile and iterative development approaches to rapidly test and refine AI concepts.

• Collaborative problem-solving, encouraging diverse perspectives to tackle complex AI challenges.

• Emphasis on continuous learning and staying abreast of rapid advancements in AI technology.

Company Website: https://www.koerber.com/

📝 Enhancement Note: The Körber Group's diverse industry focus provides a rich landscape for applied AI. The company's long history and global scale suggest a robust environment for learning and development, while the emphasis on innovation aligns with the core of this AI prototyping role.

📈 Career & Growth Analysis

Operations Career Level: This role is at the entry-level, specifically designed for students. It serves as a foundational experience in applied AI engineering and prototyping, offering a pathway to further specialization within the AI/Tech domain.

Reporting Structure: The Working Student will likely report to an experienced AI Engineer or a Team Lead within the Applied AI or Innovation department. This provides direct mentorship and guidance.

Operations Impact: While not directly managing operational processes, this role's impact is in accelerating the evaluation and adoption of new AI technologies. Successful prototypes can lead to significant improvements in efficiency, decision-making, and innovation across Körber's business areas.

Growth Opportunities:

• Deepen expertise in LLMs, agentic systems, and RAG through hands-on project work and mentorship.

• Develop practical skills in AI prototyping, tool evaluation, and technical integration.

• Gain exposure to various business applications of AI across Körber's diverse industries.

• Potential for conversion to a full-time AI Engineer or related technical role upon completion of studies and successful performance.

• Opportunities for further education and professional development within the Körber Group's tech divisions.

📝 Enhancement Note: The "Working Student" designation clearly positions this role as a developmental opportunity. The growth potential is significant, offering a direct line into specialized AI engineering roles within a large, innovative corporation.

🌐 Work Environment

Office Type: The role is on-site in Berlin, Germany, suggesting a professional office environment within the Körber Group's facilities.

Office Location(s): Berlin, Germany. This location offers access to a vibrant tech scene and a hub for innovation.

Workspace Context:

• Collaborative workspaces designed to foster teamwork and knowledge sharing among technical teams.

• Access to modern computing resources and development tools necessary for AI prototyping.

• Opportunities for direct interaction with experienced AI professionals, fostering a learning-rich environment.

• A structured yet experimental atmosphere where new ideas are encouraged and tested.

Work Schedule: Approximately 15-20 hours per week, with flexibility to align with university course schedules and exam periods. This allows for a balance between academic commitments and practical work experience.

📝 Enhancement Note: The on-site requirement in Berlin, a major European tech hub, is a key aspect of the work environment. The emphasis on collaboration and access to resources is critical for a prototyping role.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Review of application, resume, and any provided project links or code samples.

• Technical Interview: Discussion of programming experience (Python, Git), AI concepts (LLMs, RAG), and problem-solving abilities. May include a small coding exercise or scenario-based questions.

• Prototyping/Concept Discussion: Deeper dive into specific AI technologies and how you would approach building prototypes. Questions about your curiosity and willingness to experiment.

• Team/Cultural Fit Interview: Assessment of your communication style, ability to work in a team, and alignment with Körber's innovative culture.

Portfolio Review Tips:

• Highlight any personal or academic projects that demonstrate Python programming skills.

• Showcase any involvement with Git, even for personal projects, emphasizing version control practices.

• If you have experimented with LLM APIs or AI frameworks, prepare to discuss your approach and learnings.

• Be ready to articulate your thought process for tackling technical challenges and learning new technologies.

• For any project, focus on the problem, your solution, the tools used, and the outcomes or learnings. Challenge Preparation:

• Brush up on fundamental Python programming concepts and common data structures.

• Familiarize yourself with the basics of Git commands and branching strategies.

• Review core concepts of LLMs, prompt engineering, and RAG. Understand what they are and their potential applications.

• Prepare to discuss your interest in AI and why you are drawn to prototyping and experimental work.

• Think about how you approach learning new technologies and solving ambiguous problems.

📝 Enhancement Note: The interview process will likely focus on assessing foundational technical skills, a genuine interest in AI, and the capacity for independent learning and experimentation, rather than extensive prior professional experience.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python (primary), potentially others as needed for integrations.

• Version Control: Git (essential for collaborative development).

• AI/ML Frameworks: LangChain, Microsoft Agent Framework (or similar orchestration tools).

• LLM APIs: Azure OpenAI (or similar providers like OpenAI, Google AI).

• Vector Stores: pgvector (or other vector database solutions like Pinecone, Weaviate).

• Evaluation Tools: Mlflow (or similar experiment tracking and model evaluation platforms).

Analytics & Reporting:

• Tools for data preparation and analysis (e.g., Pandas, NumPy).

• Potentially dashboarding tools for visualizing prototype performance, if developed. CRM & Automation:

• Familiarity with API integration concepts is key, as prototypes may need to connect to enterprise systems.

• Understanding of basic workflow design for agentic systems.

📝 Enhancement Note: The technology stack listed reflects the core AI development tools mentioned in the job description. Proficiency or familiarity with these will be a significant advantage.

👥 Team Culture & Values

Operations Values:

• Innovation: A strong drive to explore new technologies and push the boundaries of what's possible with AI.

• Experimentation: A culture that embraces trying new ideas, learning from failures, and iterating rapidly.

• Collaboration: Working effectively with peers, sharing knowledge, and contributing to a team environment.

• Data-Driven Approach: Using empirical data from prototypes and benchmarks to inform technical decisions.

• Efficiency: Focusing on building practical, workable solutions and understanding technical trade-offs.

Collaboration Style:

• Open communication and knowledge sharing through team meetings, code reviews, and informal discussions.

• Cross-functional interaction with other technical teams and potentially business stakeholders to define project scope and testability.

• A feedback-rich environment where constructive criticism is welcomed to improve prototypes and processes.

📝 Enhancement Note: The culture emphasizes a proactive, experimental, and collaborative approach to AI development, aligning with the nature of prototyping and applied engineering.

⚡ Challenges & Growth Opportunities

Challenges:

• Navigating the rapidly evolving landscape of AI technologies and tools.

• Balancing academic commitments with the demands of a hands-on technical role.

• Dealing with ambiguity in early-stage concept exploration and prototyping.

• Integrating AI prototypes with potentially complex existing enterprise systems. Learning & Development Opportunities:

• Direct exposure to cutting-edge AI research and development in a corporate setting.

• Opportunities to gain practical experience with industry-standard AI tools and frameworks.

• Mentorship from experienced AI engineers, providing guidance on technical skills and career development.

• Potential to contribute to projects that have a real impact on Körber's business operations.

📝 Enhancement Note: The challenges are inherent to working in a fast-paced, emerging technology field, while the growth opportunities are substantial for a student looking to build a career in AI.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you had to learn a new programming language or tool quickly. How did you approach it?" (Assesses learning agility and structured learning)

• "How would you approach building a prototype to test the feasibility of using LLMs for customer support?" (Assesses problem-solving and AI application thinking)

• "What are your thoughts on the ethical considerations of using AI, particularly LLMs?" (Assesses critical thinking and awareness of broader AI implications) Company & Culture Questions:

• "What interests you about Körber Group and our work in Applied AI?" (Assesses company research and genuine interest)

• "How do you handle working on projects where the requirements are not fully defined?" (Assesses comfort with ambiguity and iterative processes)

• "Describe your ideal team environment for a technical project." (Assesses collaboration style and cultural fit) Portfolio Presentation Strategy:

• Be prepared to walk through any relevant code samples or project documentation you have.

• Clearly articulate the problem you were trying to solve, your approach, the tools you used, and the outcomes or learnings.

• If you haven't built extensive AI projects, focus on demonstrating your Python programming skills and your understanding of AI concepts.

• Be enthusiastic about your projects and your desire to learn and contribute.

📝 Enhancement Note: Interview preparation should focus on demonstrating core technical skills, a proactive learning attitude, and a genuine passion for AI and its practical applications.

📌 Application Steps

To apply for this working student position:

• Submit your application through the Körber Group's careers portal via the provided link.

• Tailor your resume: Highlight your Python programming experience, any Git usage, academic projects related to computer science/data science, and any specific exposure to AI concepts or tools mentioned in the job description.

• Prepare your portfolio: Gather any code samples, project documentation, or GitHub repositories that showcase your programming skills and interest in AI. Even academic projects are valuable.

• Research Körber Group: Understand their business areas (Supply Chain, Pharma, Industrial Automation) and their stated commitment to innovation and technology leadership.

• Practice explaining technical concepts: Be ready to discuss your programming experience, your understanding of LLMs, and how you approach learning new technologies.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

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