Work
Machine learning engineer focused on empirical AI research, scalable ML systems, and production deployment. Currently consulting at Magical Labs.
experience
Magical Labs
Consulting on production ML systems across a range of client domains. Built a steganography system achieving 96.8% bit recovery under JPEG compression, embedding hidden messages in Stable Diffusion images via DDIM noise modulation. Deployed a recommendation system on AWS OpenSearch + Celery for a 200k MAU mobile games marketplace, increasing revenue-per-click by 23%. Led a predictive analytics dashboard for a $10B real estate portfolio that cut monthly reporting time by 95%.
Stealth Startup
Fine-tuned GPT-2 for generative percussion modeling using FSDP and PEFT for professional music production. Engineered a high-performance C++ MIDI tokenizer and real-time inference pipeline with ONNX Runtime, achieving a 10× CPU speed-up across major DAWs.
Media Instinct Group
Reduced Spark ETL job execution time by 67% (6 → 2 hours) by optimizing execution plans at scale. Built an ARIMA time series model forecasting GRP for commercial media buying, directly informing strategy for 50+ corporate clients.
MaximaTelecom
Built an NLP pipeline classifying unstructured text into 80+ categories for an expense management service. Engineered an XGBoost pipeline deriving socio-demographic signals from raw data for 5M+ users, improving prediction accuracy by 12%.
education
Lomonosov State University
Research in extreme value theory — rate of convergence in limit theorems for maxima of random variables, conducted in the Laboratory of Probability.
skills
Languages
Python, C++, Scala, SQL, Bash
Frameworks
PyTorch, Keras, FSDP, Hugging Face, ONNX Runtime, Scikit-learn, XGBoost
Infrastructure
AWS, Apache Spark, Airflow, Docker, MLflow, Celery, Elasticsearch, Postgres