FY26 Working Student - GenAI Prototyping - 6 months, Munich, Germany

📍 Job Overview

Job Title: FY26 Working Student - GenAI Prototyping - 6 months

Company: Qualcomm Technologies GmbH

Location: Munich, Bavaria, Germany

Job Type: Intern, Part-Time, Full-Time

Category: Engineering / Technology - AI/Machine Learning / Software Development

Date Posted: April 15, 2026

Experience Level: Entry-Level (Internship)

Remote Status: Hybrid

🚀 Role Summary

• This role focuses on developing and enhancing prototypical applications leveraging Generative AI (GenAI) within Qualcomm's Automotive sector.

• Key responsibilities involve supporting the integration of GenAI across the V-cycle development process, from requirement engineering to full-stack software solutions.

• The position requires innovation and proactive problem-solving, with an emphasis on developing visionary applications using Large Language Models (LLMs) and advanced Prompt Engineering techniques.

• A significant aspect of the role involves enhancing Retrieval-Augmented Generation (RAG) systems by integrating GraphDB and AI agents for improved performance in query generation, indexing, and prompt optimization.

📝 Enhancement Note: While the title specifies "Working Student," the employment type list includes "FULL_TIME," indicating flexibility to accommodate either full-time internship hours or part-time student schedules, common for such roles in Germany. The core focus is on practical application development in GenAI within an automotive context, aligning with roles in operations technology and R&D.

📈 Primary Responsibilities

• Develop and refine prototypical applications utilizing Generative AI (GenAI) to enhance engineering workflows in requirement, release, and quality assurance within the Automotive ADAS/AD stack development.

• Research, implement, and experiment with the latest Large Language Models (LLMs) and novel Prompt Engineering techniques to create cutting-edge applications.

• Take end-to-end ownership of assigned software projects, managing the workflow from initial requirement definition through to the delivery of a functional full-stack software solution.

• Proactively identify and address challenges, drive project progress independently, and contribute fresh ideas and solutions to the team.

• Enhance existing RAG systems by integrating Graph Database (GraphDB) and AI agents to implement advanced techniques, focusing on query generation, indexing, retriever reranking, prompt optimization, and dynamic pipeline switching to boost retrieval accuracy and overall system performance.

• Support the broader integration of GenAI tools and methodologies across various development stages of the V-cycle.

📝 Enhancement Note: The responsibilities clearly indicate a hands-on development role with a strong emphasis on R&D and prototyping. For operations professionals, this translates to understanding how new AI technologies can be practically applied to improve efficiency and effectiveness in engineering processes, particularly in areas like requirements management and quality assurance.

🎓 Skills & Qualifications

Education:

Experience:

• Experience in software development, with a strong emphasis on Python programming.

• Practical experience with Generative AI frameworks and libraries such as Flask, FastAPI, LangChain, or React.

• Familiarity with RAG (Retrieval-Augmented Generation) systems, GraphDB, and Requirement Engineering principles.

Required Skills:

• Python Programming: Demonstrable proficiency for developing GenAI applications and integrating various systems.

• Generative AI Frameworks: Hands-on experience with tools like LangChain, Flask, FastAPI, or similar for building AI-powered applications.

• RAG Systems: Understanding and practical experience in developing or enhancing RAG pipelines for improved information retrieval.

• GraphDB Integration: Experience with integrating Graph Databases to support AI applications, particularly for complex data relationships.

• Prompt Engineering: Ability to design, test, and optimize prompts for effective LLM interaction.

• Software Development Lifecycle: Familiarity with the end-to-end software development process, from requirements to deployment.

Preferred Skills:

• Experience with AI agents and their integration into larger systems.

• Knowledge of ADAS/AD (Advanced Driver-Assistance Systems/Autonomous Driving) stack development within the automotive industry.

• Familiarity with requirement engineering tools and methodologies.

• Experience with front-end development frameworks like React for building user interfaces.

• German language skills (though English proficiency is also accepted).

📝 Enhancement Note: The emphasis on Python, GenAI frameworks, RAG, and GraphDB highlights a need for technical skills directly applicable to building and optimizing AI-driven solutions. For operations roles, this translates to understanding how to leverage these technologies for process automation, enhanced data analysis, and improved decision-making support. The "0-2 years" AI experience level suggests that practical project work or academic experience in these areas is highly valued.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase of 2-3 personal or academic projects demonstrating proficiency in Python development and AI/GenAI application building.

• Projects should highlight experience with at least one Generative AI framework (e.g., LangChain, Flask, FastAPI) and RAG systems.

• Include examples of prompt engineering techniques applied to achieve specific outcomes.

Process Documentation:

• For each project in the portfolio, provide a brief explanation of the development workflow, including:
  • Requirement Definition: How project goals were identified and defined.
  • Design & Architecture: Key architectural decisions and system design.
  • Implementation: Challenges faced and solutions implemented during development.
  • Testing & Refinement: How the application was tested and improved, particularly regarding AI model performance or retrieval accuracy.
  • Outcome & Learnings: The final results, impact, and key takeaways from the project.

📝 Enhancement Note: Given this is an internship/working student role focused on prototyping, the portfolio is less about formal operations processes and more about demonstrating practical technical application and problem-solving skills in GenAI development. The emphasis is on initiative and the ability to take a project from concept to a working prototype.

💵 Compensation & Benefits

Salary Range:

• Estimated Range: €20 - €25 per hour (approx. €3,200 - €4,000 per month for full-time, depending on exact hours and experience).
  • Rationale: This estimate is based on typical working student (Werkstudent) compensation in Germany for technical roles in major technology companies, particularly in Munich. Rates for interns and working students in Germany are generally structured hourly and can vary based on the company, industry, and specific technical skills required. Munich has a higher cost of living, which also influences compensation. The "Attractive remuneration" benefit confirms a competitive offering.

Benefits:

• Comprehensive Mentoring & Onboarding: Structured support to integrate into the team and projects effectively.

• Personal & Professional Development: Opportunities for skill enhancement and career growth.

• Flexible Working Hours: Adaptable schedules to accommodate academic commitments.

• Digital Offers & Mobile Working: Access to digital tools and potential for remote work arrangements.

• Attractive Remuneration: Competitive payment for the internship/working student position.

• Work in the Automotive Sector: Gain exposure to cutting-edge automotive technology and development processes.

Working Hours: Part-time & Full-time (flexible, up to 40 hours/week, to be agreed upon).

📝 Enhancement Note: The salary estimate is derived from standard German "Werkstudent" rates for technical internships in high-cost-of-living areas like Munich. The provided benefits are standard for large tech companies and are particularly valuable for interns seeking development and flexibility.

🎯 Team & Company Context

🏢 Company Culture

Industry: Semiconductors / Technology / Automotive

Company Size: Qualcomm is a large, global technology company (over 50,000 employees). This means access to extensive resources, established processes, and a wide network of professionals, but also potentially more structured workflows.

Founded: Qualcomm was founded in 1985. This long history signifies stability, deep technological expertise, and a proven track record in innovation, particularly in mobile communications and expanding into areas like automotive.

Team Structure:

• Operations Focus: While this is an engineering/R&D role, it's embedded within the Automotive sector, specifically ADAS/AD stack development. The "operations" aspect comes from how GenAI is being used to improve the operations of engineering teams (requirement, release, quality).

• Cross-functional Collaboration: The role will likely involve collaboration with other engineering teams, AI/ML specialists, and potentially product managers to define and prototype use cases.

• Innovation Hub: The team appears to be a forward-thinking group actively exploring and implementing cutting-edge AI technologies.

Methodology:

• Prototypical Development: Focus on rapid development and iteration of AI-driven applications.

• RAG System Enhancement: Deep dive into improving information retrieval and AI agent capabilities.

• Agile/Iterative Approach: Likely to follow agile principles for project management and development cycles.

• Data-Driven Improvement: Emphasis on enhancing retrieval accuracy and system performance through data and advanced techniques.

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

📝 Enhancement Note: Qualcomm's established presence in technology, especially mobile, provides a strong foundation. The automotive division is a significant growth area, and the focus on GenAI for engineering process improvement is a modern, operations-centric application of AI. The team is likely composed of skilled engineers and researchers passionate about AI and its practical implementation.

📈 Career & Growth Analysis

Operations Career Level: This is an entry-level, internship/working student position. It's designed for individuals early in their technical career, providing foundational experience in AI prototyping and software development within a major tech company.

Reporting Structure: The intern/working student will report to a team lead or senior engineer within the Automotive GenAI Prototyping group. The structure is likely supportive, with a focus on learning and mentorship.

Operations Impact: While not a traditional operations role, the work directly impacts the efficiency and effectiveness of engineering operations. By developing GenAI tools, this role aims to streamline processes, improve data access for engineers, and potentially accelerate development cycles within the automotive division.

Growth Opportunities:

• Technical Skill Deepening: Significant opportunity to build expertise in Python, GenAI frameworks, RAG, GraphDB, and prompt engineering.

• Industry Exposure: Gain practical experience in the rapidly evolving field of AI within the high-stakes automotive industry.

• Mentorship: Benefit from guidance from experienced engineers and AI specialists at Qualcomm.

• Project Ownership: Develop skills in managing a project from conception to prototype, including requirement gathering and full-stack development.

• Networking: Build connections within Qualcomm's engineering community, potentially leading to future internship or full-time opportunities.

📝 Enhancement Note: This role is a stepping stone. It offers a chance to apply academic knowledge to real-world challenges, gain exposure to corporate R&D environments, and develop a portfolio of AI-related projects. For students interested in GTM operations, understanding how R&D uses AI to improve internal processes provides valuable context.

🌐 Work Environment

Office Type: Hybrid. Qualcomm offers flexible work arrangements, allowing for a mix of in-office and remote work. The Munich office is a key hub for their European operations.

Office Location(s): Munich, Bavaria, Germany. This is a major European hub for Qualcomm, suggesting a well-equipped and modern workspace.

Workspace Context:

• Collaborative Environment: Expect a team-oriented workspace that encourages interaction and knowledge sharing, especially for prototyping and innovation.

• Access to Technology: Will likely have access to high-performance computing resources, development tools, and the latest software relevant to AI and GenAI development.

• Team Interaction: Opportunities to work closely with senior engineers and researchers, facilitating learning and problem-solving.

Work Schedule: Flexible working hours are offered, ideal for students balancing academic responsibilities with work. The ability to work part-time or full-time provides further flexibility.

📝 Enhancement Note: The hybrid nature and flexible hours are typical for modern tech companies and are particularly beneficial for student roles. The Munich office environment is likely to be modern and geared towards fostering innovation and collaboration.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Resume and application review, likely focusing on academic background, technical skills (Python, AI frameworks), and any relevant project experience.

• Technical Assessment: This may involve a coding challenge (e.g., Python-based problem), a discussion about AI/GenAI concepts, or a review of a candidate's GitHub profile/portfolio.

• Interview with Hiring Manager/Team: Deeper dive into experience, technical skills, problem-solving approach, and motivation for the role. Questions will likely assess understanding of GenAI, RAG, and software development best practices.

• Cultural Fit Assessment: Evaluation of proactivity, independence, problem-solving mindset, and ability to work in a team.

Portfolio Review Tips:

• Highlight Relevant Projects: Focus on projects that demonstrate Python proficiency, experience with GenAI frameworks (LangChain, etc.), RAG implementation, and prompt engineering.

• Showcase Problem-Solving: For each project, clearly articulate the problem you were trying to solve, your approach, the technical challenges, and how you overcame them.

• Quantify Impact (if possible): Even for prototypes, try to mention any improvements in efficiency, accuracy, or novelty achieved.

• Code Quality & Documentation: Ensure your code is clean, well-commented, and accessible (e.g., via GitHub).

• Presentation Readiness: Be prepared to walk through your projects, explain your technical decisions, and answer detailed questions about your implementation.

Challenge Preparation:

• Python Fundamentals: Brush up on core Python concepts, data structures, and algorithms.

• GenAI Concepts: Review LLMs, RAG principles, prompt engineering techniques, and common frameworks like LangChain.

• System Design Basics: Understand how to design simple software systems, including API interactions and database integration.

• Problem-Solving: Practice breaking down complex problems into smaller, manageable parts.

📝 Enhancement Note: For this role, the portfolio is critical for demonstrating practical skills. Candidates should curate projects that directly align with the requirements, showing initiative and technical capability beyond coursework. The interview will assess not only technical knowledge but also the candidate's proactive and innovative mindset.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Language: Python (primary)

• Generative AI Frameworks: LangChain, potentially others like LlamaIndex.

• Web Frameworks: Flask, FastAPI for building API services.

• Front-end Frameworks: React for building user interfaces.

• Databases: GraphDB (e.g., Neo4j, ArangoDB), potentially SQL or NoSQL databases for data storage.

• AI/ML Libraries: PyTorch, TensorFlow (indirectly, via LLM usage).

• Version Control: Git (essential for code management).

Analytics & Reporting:

CRM & Automation:

• Not directly applicable in this R&D prototyping context, but understanding how GenAI can automate tasks in other areas (like requirement analysis) is relevant.

📝 Enhancement Note: The technology stack is heavily skewed towards modern AI development tools. Proficiency in Python is paramount, followed by experience with GenAI libraries and data integration technologies like GraphDB. This stack is representative of cutting-edge AI development used to enhance operational efficiency.

👥 Team Culture & Values

Operations Values:

• Innovation & Proactivity: A strong emphasis on bringing new ideas, independently driving projects, and proactively solving problems.

• Technical Excellence: Valuing deep technical understanding and practical application of AI/ML principles.

• Collaboration & Knowledge Sharing: Encouraging teamwork and the open exchange of ideas and solutions.

• Impact-Oriented: Focusing on developing solutions that demonstrably improve engineering processes and outcomes.

• Continuous Learning: Staying abreast of the rapidly evolving field of Generative AI.

Collaboration Style:

• Team-Based Prototyping: Working closely with team members to brainstorm, develop, and iterate on AI prototypes.

• Cross-Functional Interaction: Engaging with engineers from different domains (e.g., ADAS/AD) to understand their needs and integrate AI solutions.

• Mentorship-Driven: Senior members actively guide and support interns/working students.

• Open Communication: Encouraging open feedback and discussion on technical approaches and project progress.

📝 Enhancement Note: The culture seems geared towards a fast-paced, innovative environment where individuals are empowered to take initiative. For an intern, this means being proactive, curious, and eager to learn and contribute. The alignment with "operations" is in how the team uses technology to make engineering operations more efficient.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapidly Evolving Field: Keeping up with the constant advancements in LLMs, GenAI techniques, and prompt engineering.

• Prototyping Scope: Balancing the desire for innovative features with the need to deliver functional prototypes within a defined timeframe (6 months).

• Integration Complexity: Successfully integrating GraphDB and AI agents into RAG systems for optimal performance can be technically challenging.

• Defining Use Cases: Identifying and scoping impactful GenAI use cases for engineering operations that are feasible to prototype.

Learning & Development Opportunities:

• Hands-on GenAI Development: Gaining practical experience building and deploying AI applications.

• Advanced AI Techniques: Deep dive into RAG, AI agents, prompt optimization, and potentially foundational LLM concepts.

• Industry-Specific AI: Understanding how AI is applied in the automotive sector for ADAS/AD development.

• Full-Stack Development: Opportunity to practice end-to-end software development, from backend APIs to frontend interfaces.

• Professional Networking: Building relationships within a leading technology company.

📝 Enhancement Note: The challenges are inherent to working with cutting-edge technology. The growth opportunities are significant, offering a chance to develop highly sought-after skills in AI and software development within a major industry player.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex technical problem you've solved. What was your approach, and what was the outcome?" (Focus on problem-solving and technical depth).

• "How would you approach designing a RAG system for a specific use case, and what are the key components you'd consider?" (Assesses understanding of RAG architecture and practical application).

• "Walk me through a project from your portfolio. Explain your role, the technologies used, and the challenges you faced." (Tests ability to articulate technical work and project ownership).

Company & Culture Questions:

• "Why are you interested in working at Qualcomm, specifically in our Automotive AI prototyping team?" (Assess motivation and alignment with company/team goals).

• "How do you stay updated with the latest developments in AI and Generative AI?" (Shows commitment to continuous learning).

Portfolio Presentation Strategy:

• Storytelling: Frame your projects as narratives – problem, solution, execution, and impact.

• Technical Clarity: Be prepared to explain the technical architecture, code logic, and specific implementation details clearly.

• Tool Proficiency: Demonstrate your familiarity with the tools and frameworks listed in the job description.

• Enthusiasm for AI: Convey genuine interest in GenAI and its potential applications.

• Conciseness: Present your key projects efficiently, focusing on the most relevant aspects.

📝 Enhancement Note: Candidates should be ready to discuss their technical projects in detail, demonstrating both theoretical understanding and practical application of GenAI and related technologies. Highlighting proactivity and a passion for innovation will be key.

📌 Application Steps

To apply for this operations-adjacent technology position:

• Submit your application through the Qualcomm Careers portal using the provided job URL.

• Curate Your Portfolio: Select 2-3 projects that best showcase your Python skills, GenAI framework experience (LangChain, Flask, FastAPI), RAG implementation, and prompt engineering abilities. Ensure code is clean and accessible (e.g., on GitHub).

• Tailor Your Resume: Emphasize relevant coursework, academic projects, and any internship experience related to software development, AI, or data science. Use keywords from the job description (Python, GenAI, RAG, Prompt Engineering, etc.).

• Prepare for Technical Discussion: Be ready to discuss your portfolio projects in detail, explain your technical choices, and answer questions about GenAI concepts. Review core Python concepts and AI fundamentals.

• Research Qualcomm & Automotive AI: Understand Qualcomm's role in the semiconductor and automotive industries. Consider how GenAI can impact engineering operations within ADAS/AD development.

⚠️ 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.