strong knowledge of large language models (LLMs), foundation models, and deep learning, combined with hands-on experience in fine-tuning (e.g. SFT, RLHF/RLAIF) or parameter-efficient adaptation methods such as LoRA or adapters - o experience with retrieval-augmented generation (RAG), including dense retrieval, reranking, and advanced architectures, as well as the integration of knowledge graphs, ontologies, or other knowledge engineering approaches (ideally with SPARQL, Cypher, or KG embeddings) o strong programming skills in Python and experience with machine learning frameworks, ideally complemented by knowledge of reinforcement learning (e.g. RL, RLHF, policy optimization) o experience in evaluating agentic systems using relevant frameworks or benchmarks, ideally complemented by contributions to scientific publications
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