Data Scientist & AI Engineer
Data Science and AI Engineering professional with a proven track record of delivering end-to-end solutions from ideation to production. I combine deep technical expertise in classical ML, NLP, and Generative AI (RAG, LLM orchestration and evaluation) with strong system design skills to build scalable, production-grade data solutions. I translate complex data insights into tangible outcomes for both technical and non-technical stakeholders — and thrive in fast-paced environments.
📫 vmanolov2000@gmail.com | 💼 LinkedIn
A simulated multi-agent Retrieval-Augmented Generation system demonstrating intelligent query routing, agent coordination, and response synthesis across multiple knowledge domains. Built to explore production-grade agentic architectures.
Python RAG LangChain Agentic Workflows Vector DBs
Data-driven system for automatically detecting line-breaking passes in football, introducing a novel metric — the Pass Advantage Score (PAS) — to evaluate the true offensive impact of passes. Combines tracking and event data with unsupervised learning to dynamically model defensive formation lines. Developed during my time at AZ Alkmaar FC.
Python Unsupervised ML Spatiotemporal Data Sports Analytics
- Building AI-native systems that integrate seamlessly into business workflows
- Exploring agentic architectures and autonomous decision systems
- Designing scalable, production-grade ML systems
- Bridging the gap between technical execution and business impact
University of Amsterdam, NL — MSc Information Studies: Data Science
University of Surrey, UK — BSc Computer Science, First Class Honours
- Teaching Assistant in Artificial Intelligence
- Teaching Assistant in Web and Database Systems
- Hackathon Winner — AFC Ajax
- AI Engineer (Production Track)
- Stanford — Statistical Learning with Python
- IBM Machine Learning Specialization
- Microsoft Azure Fundamentals (AZ-900)
English — Native | Bulgarian — Native | German — Professional