From Cognitive Science to Production AI
The thread through every role: building intelligent systems that work reliably in the real world.
Where Curiosity Met Code
It started with a simple question: can machines think like us? A Computer Science degree gave me the tools. A Master's in Cognitive Systems — at the intersection of AI, neuroscience, and human-computer interaction — gave me the obsession. Not just building software, but building systems that perceive, learn, reason, and adapt.
Teaching Robots to See & Learn
At Fraunhofer IPA, I worked on making robots collaborate safely with humans in dynamic environments. I applied contrastive unsupervised learning to extract representations from high-dimensional image data — no labels needed. These latent features supercharged deep reinforcement learning policies, cutting latency and accelerating robot learning. This was AI beyond simulations — real-time, physical, unforgiving.
Predicting Hardware Failures Before They Happen
At Nokia, the challenge shifted to predicting chip failures under extreme voltage and temperature conditions — catching problems before final testing. I unified data from multiple sensor sources into a single pipeline, trained models from Random Forest to neural networks, and built dashboards that turned complex QA data into decisions engineers could act on.
Owning the Full ML Lifecycle
Working with Volkswagen through Reply, I took end-to-end ownership — designing and deploying a complete ML pipeline for automotive sales prediction on AWS. I built ETL pipelines that improved prediction accuracy by ~30%, introduced CI/CD for deep learning experiments with Terraform (cutting iteration time in half), and led a cross-account data migration with zero downtime. Over 1.5 years, I owned requirements, timelines, and execution independently.
Anomaly Detection Where Failure Isn't an Option
Satellite telemetry data. Time-series patterns. Subtle deviations that could signal mission-critical failures. At Solenix, precision wasn't a goal — it was the baseline. I built anomaly detection models that identified system deviations early, contributing to systems where early detection is the difference between mission success and catastrophic loss.
Bridging AI and Business Impact
Now as an AI Integration Consultant, I help organizations move from "AI sounds interesting" to "AI is driving results." I design and implement solutions that bridge business needs with ML capabilities — practical, scalable adoption over experimental hype. From RAG pipelines to risk analytics, the focus is always on measurable impact.
Research Depth. Production Rigor.
I move seamlessly between research and production — translating advanced ML concepts into robust, real-world applications that create measurable impact. From robots learning in dynamic human environments to deploying scalable pipelines in production, every role sharpened a different edge of my toolkit.