CV and Full Skills

Extended competency overview beyond the homepage stack subset.

Profile Snapshot

Data Scientist and ML Engineer with dual-domain background and applied AI delivery focus.

MSc in Applied Information and Data Science (HSLU, GPA 5.6/6) and MSc Physiotherapy (BFH, Best Graduate 2022, GPA 5.6/6).

Master thesis: Graph-aware RAG for Innovation Scouting (ETH Zurich / Swiss Data Science Center), with findings presented in technical talks.

Current role includes data interoperability work, DS workspace design, LLM/RAG PoCs, edge deployment, and GPU inference on secure internal infrastructure.

Programming Languages

Python

SQL

Bash

Data Ingestion and Scraping

BeautifulSoup and HTML parsing

API-based data acquisition

PDF parsing with OCR

Bronze/Silver/Gold pipelines

Typer and Click CLI tooling

LLMs and RAG

Hybrid dense+sparse retrieval (BM25 + vector search)

Cross-encoder reranking (Flashrank)

Hugging Face ecosystem (Transformers / Hub)

Prompting, chunking, context fusion

Citation-aware answer generation

Vector Databases and GraphRAG

ChromaDB and BM25 index design

Neo4j and knowledge-graph retrieval

Milvus, Weaviate, Qdrant, Pinecone

FAISS and pgvector indexing

Fine-Tuning and Model Work

PEFT with LoRA and QLoRA

Quantization and efficient serving

Model and prompt evaluation

Embedding and reranker selection

HF-based model management

Telemetry and Monitoring

OpenTelemetry and Prometheus

Metrics, tracing, and logging

Health checks and alerting hooks

Latency, throughput, token-cost tracking

Retrieval and answer-quality monitoring

Networking and Security

Zero-trust overlay networking

Isolated on-prem environments

API-key and service authentication

Offline operation with secure model caches

Agentic and Multimodal AI

Tool-calling workflows

Planner/executor orchestration patterns

Multi-agent design patterns

Text, image, and audio pipeline integration

APIs and Deployment

FastAPI and Uvicorn

Docker and Docker Compose

Kubernetes

vLLM serving (OpenAI-compatible endpoints)

GPU scaling on NVIDIA hardware

Deployment via Dokploy

VM/VPS and edge-oriented deployments