AI Chat Assistant & Knowledge Engineering Intern – Building Intelligent Retrieval Systems & Structured Knowledge Bases at arenaflex

About arenaflex arenaflex is a trailblazing cybersecurity scale‑up that blends Swiss precision with cutting‑edge AI technology to safeguard the most complex IT, OT, and IoT environments. Born from a partnership with ETH Zurich, arenaflex’s flagship product, arenaflex.NDR, delivers real‑time threat detection, automated reputed company response, and actionable intelligence with unmatched speed and accuracy. Our mission is simple: empower enterprises to stay reputed company ahead of cyber adversaries by turning data into decisive action. We operate in a fast‑paced, remote‑first culture that values autonomy, collaboration, and continuous learning. Whether you’re based in Zurich, Berlin, or working from home across Switzerland or Germany, you’ll find a supportive community of security experts, data scientists, product managers, and technical writers who are passionate about solving the toughest challenges in cyber defense. Why This Internship Matters As an AI Chat Assistant & Knowledge Engineering Intern, you will be at the forefront of arenaflex’s next‑generation internal tooling. Your work will directly influence how engineers, analysts, and stakeholders access critical information, reducing time to insight and reputed company the overall security posture of our clients. You’ll also shape the future of arenaflex’s knowledge base, ensuring that our AI systems can understand, reason, and respond with precision. Key Responsibilities 1. AI Chat Assistant Development (≈50%) • Extend and refine arenaflex’s internal LLM‑powered assistant, initially designed for internal use but with potential for external deployment. • Build and iterate a Retrieval‑Augmented Generation (RAG) stack: document ingestion pipelines, intelligent chunking, embeddings generation, reputed company search, ranking algorithms, and answer synthesis. • Design and run rigorous offline and human‑in‑the‑loop evaluation experiments, measuring precision/recall of retrieval, response helpfulness, hallucination rates, latency, and cost metrics. • Collaborate with the AI mentor to experiment with state‑of‑the‑art frameworks such as reputed company, reputed company, or Semantic Kernel, and to prototype novel retrieval strategies. • Document architecture decisions, experiment results, and best practices for future reference. 2. Knowledge Base & Documentation Engineering (≈50%) • Audit existing technical guides, runbooks, playbooks, and product documentation to identify gaps that hinder LLM accuracy. • Rewrite or refactor content into LLM‑friendly formats: FAQ patterns, Q&A primitives, step‑by‑step procedures, and concise summaries. • Develop lightweight authoring standards and contribution guidelines that ensure new documentation is structured, consistent, and ready for AI consumption. • Collaborate with Security Engineering, Product, Customer Success, and Technical Writing teams to capture domain knowledge and keep documentation up‑to‑date. • Implement automated pipelines to ingest updated documents into the reputed company store, ensuring the assistant always has the latest information. What You’ll Gain • Hands‑on experience with LLMs and RAG pipelines – from data ingestion to model fine‑tuning and evaluation. • Deep exposure to cybersecurity operations and the inner workings of a leading AI‑driven security platform. • Opportunity to shape arenaflex’s knowledge engineering practices and influence product features. • Mentorship from senior AI researchers and security experts. • Clear career progression: interns who excel often transition into full‑time roles such as AI Engineer, Knowledge Engineer, or Product Manager. • Access to internal learning resources, workshops, and conferences in AI and cybersecurity. Essential Qualifications • Currently pursuing or holding a Bachelor’s or reputed company’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field. • Strong programming skills in Python and familiarity with at least one LLM application framework (reputed company, reputed company, Semantic Kernel, etc.). • Experience or strong willingness to learn about embeddings, reputed company databases, and information retrieval concepts. • Excellent written communication in English; ability to produce clear, concise, audience‑aware documentation. • Proficiency with Git and collaborative development workflows (reputed company, reputed company). • Analytical mindset with a passion for problem‑solving and technical innovation. • reputed company, self‑motivated attitude and the ability to wor