MSc Data Science, University of Europe for Applied Sciences, Potsdam. My thesis discovered the optimal fusion weight for hybrid retrieval — alpha = 0.70, not the commonly assumed 50/50 split — validated against 8.84M passages. Since then I've shipped eight production AI systems, alone, end to end.
I research how retrieval actually works, then ship it. Most hybrid RAG tutorials assume sparse and dense retrieval should be weighted 50/50. My thesis tested that assumption directly — eleven tracked experiments later, the real optimum was alpha=0.70, validated with a paired t-test (p=0.002). That single number improved Recall@10 by 11.4% over the naive baseline.
After graduating I kept building instead of stopping at the thesis: fine-tuning LLMs with QLoRA on a free Colab GPU, orchestrating four-agent LangGraph pipelines, deploying to AWS EC2 with systemd, running Kubernetes with zero-downtime rollouts, and tracking every experiment publicly on MLflow.
I'm now looking for an ML Engineer, NLP Engineer, or AI Engineer role in Berlin. Job Seeker Visa holder, EU Blue Card eligible, available immediately.
Most hybrid RAG implementations split sparse (BM25) and dense (embedding) retrieval 50/50 by default. I ran eleven systematic experiments across the fusion weight and found the true optimum sits at alpha=0.70 in favour of dense retrieval — confirmed with a paired t-test, p=0.002. That one calibration choice is worth +11.4% Recall@10 over the naive split, on a corpus of 8.84M passages.
Actively seeking ML Engineer, NLP Engineer, and AI Engineer roles in Berlin. Job Seeker Visa holder, EU Blue Card eligible. I bring eight deployed systems and a habit of debugging production ML infrastructure until it actually works.