€500 per month
Key Skills
Job Description
We are looking for motivated AI Engineering Interns to join an exciting industrial AI project in collaboration with an international Engineering, Procurement, and Construction (EPC) company. About the Project The goal of this project is to design and develop a locally deployed AI assistant powered by Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). The assistant will help engineering professionals interact with internal technical documentation, retrieve relevant information, and automate knowledge-intensive workflows. Unlike cloud-based AI solutions, the system will run locally to support data privacy, security, and deployment in restricted IT environments. You will have the opportunity to work on a real-world AI product, from initial prototyping to deployment and evaluation. Your Responsibilities Design and implement RAG pipelines for retrieving information from engineering documents. Integrate and optimize open-source LLMs for local deployment. Develop document processing pipelines for PDFs, technical manuals, specifications, and other engineering documentation. Experiment with embedding models, vector databases, retrieval strategies, and reranking techniques. Build AI agent capabilities for engineering-related tasks and workflows. Evaluate system performance, including retrieval accuracy, answer quality, latency, and reliability. Collaborate with engineers to understand requirements, collect feedback, and improve the solution. Contribute to the development of a user-friendly interface and backend services. Mandatory requirements: Currently enrolled as a student at a university or university of applied sciences in the Netherlands, with student status maintained throughout the internship. Available for a 9–12 month internship. Proficiency in Chinese (Mandarin), with the ability to communicate effectively with Chinese-speaking engineering teams. Background in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field. Basic understanding of machine learning, NLP, and LLMs. Strong problem-solving skills and willingness to learn. Nice to Have: Experience with RAG frameworks such as LangChain, LlamaIndex, or Haystack. Familiarity with local LLM inference tools such as Ollama, llama.cpp, or vLLM. Experience with vector databases such as FAISS, Chroma, Qdrant, or Milvus. Knowledge of Docker, Git, REST APIs, or backend development. Experience with document parsing, OCR, or multimodal AI. Familiarity with engineering documentation or EPC industry workflows. You do not need to have experience with every technology listed above. We value curiosity, initiative, and the ability to learn independently. What We Offer Internship compensation of €500 per month. Hands-on experience building a real-world AI product for an international Engineering, Procurement, and Construction (EPC) company. Practical experience with modern LLMs, RAG architectures, AI agents, and local AI deployment. Possibility to align the internship with university internship or graduation requirements, subject to university approval.
Core Responsibilities
Design and develop a locally deployed AI assistant, including RAG pipelines, document processing, open-source LLM integration, and AI agent capabilities for engineering workflows. Evaluate retrieval accuracy, answer quality, latency, and reliability, and collaborate with engineers to improve the product and its interface and backend.
Requirements
Applicants must be enrolled at a university or university of applied sciences in the Netherlands and maintain student status throughout a 9–12 month internship. They need Mandarin proficiency, a relevant academic background, basic knowledge of machine learning, NLP, and LLMs, and strong problem-solving skills; relevant tools and industry experience are advantageous but not required.
Benefits
- Hands-On Experience Building a Real-World AI Product
- Practical Experience With Modern LLMs, RAG Architectures, AI Agents, and Local AI Deployment
- Possibility to Align the Internship With University Internship or Graduation Requirements, Subject to University Approval
About ExplorAI
Industry: IT Services and IT Consulting
Company size: 2-10 employees
ExplorAI was founded on the belief that artificial intelligence holds tremendous potential to improve our world—but only when developed and deployed responsibly. As the European Union's AI Act establishes the world's first comprehensive regulatory framework for AI systems, we saw a critical need for independent, expert assessment bodies that could bridge the gap between regulatory requirements and practical implementation. Our team brings together decades of combined experience in AI technology, regulatory compliance, and certification processes. We understand both the technical complexities of AI systems and the nuances of regulatory frameworks, allowing us to provide assessments that are thorough, fair, and genuinely helpful to organizations navigating this new landscape. We believe that compliance should not be a burden but an opportunity—a chance to build better AI systems, earn stakeholder trust, and contribute to a future where AI benefits everyone. Our mission is to make that vision a reality by providing the guidance, expertise, and independent validation that organizations need to succeed. ExplorAI provides independent EU AI Act assessments, high-risk AI pre-assessments and practical EU AI Act training for organisations preparing AI systems for compliance. Accreditation Status ExplorAI is preparing for official designation as an EU AI Act Notified Body. We operate under ISO/IEC 17065 principles of independence and impartiality, ensuring that all our assessments meet the highest international standards for conformity assessment. Our preparation process includes comprehensive quality management systems, technical competence verification, and alignment with all relevant EU regulatory requirements. We are committed to complete transparency about our accreditation journey and will update this information as we progress through the notification process.