Generative AI Engineer with less than a year in Multi-Agent Systems & RAG
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Assessing your cultural and operational fit
Generative AI Engineer Intern with hands-on experience in building multi-agent SEO content generation systems and RAG-augmented content writers. Proven ability to design and implement complex AI agents for content strategy, keyword classification, and quality assurance, integrating end-to-end pipelines with various APIs. Skilled in Python, C++, and leading AI/ML frameworks, with expertise in developing innovative solutions for real-world problems such as patent search.
GISMA University of Applied Sciences
MSc. · Data Science, AI and Digital Business
September 1, 2024 – Present
Eneto GmbH
GenAI Engineer Intern
December 1, 2025 – Present
Berlin, Berlin, Germany
PatentSearch - Electric Vehicle Battery RAG Search System
August 1, 2025 – Present
Built RAG system for electric vehicle battery patents that finds similar existing patents to assess novelty. Implemented parent-child document splitting to separate patent claims from descriptions for better search. Created Streamlit interface where users upload EV battery patent PDFs or enter text descriptions for similarity search. Deployed hybrid search combining vector similarity and BM25 keyword matching for technical terms and semantic relevance.
Cultural Fit Analysis
The candidate's project diversity, specifically the multi-agent SEO content generation system and the EV battery patent RAG system, demonstrates a broad interest and applicability of Generative AI across different domains. The current internship as a GenAI Engineer Intern at Eneto GmbH directly aligns with the target role, indicating a clear career path and commitment to the field. The listed skills and frameworks are highly relevant to modern Generative AI development, suggesting a good fit for a forward-thinking technical team. The ongoing Master's degree in Data Science, AI, and Digital Business further reinforces a strong academic foundation and continuous learning mindset.
Soft Skills & Operational Fit
The candidate's resume highlights strong problem-solving skills through the design of complex multi-agent systems and RAG implementations. The description of replacing a 4-5 hour manual workflow with an autonomous system suggests an aptitude for efficiency and process improvement. The integration with various external tools indicates an ability to work within broader operational contexts. However, without direct assessment data, specific soft skills like teamwork, stress handling, or communication clarity in a collaborative setting cannot be fully evaluated.