I offer end‑to‑end Machine Learning solutions that go beyond prototypes and into production. My focus is on building reliable, explainable, and maintainable AI systems that solve real business problems.

Specifically, I offer:

- MLOps & Production ML Systems

- Design and implementation of full ML pipelines: data versioning, experiment tracking, model training, evaluation, and deployment.

- Building scalable FastAPI-based ML services, containerized with Docker and integrated with CI/CD (GitHub Actions).

- Monitoring and model quality checks using Prometheus, Grafana, and EvidentlyAI to detect drift and performance issues.

- RAG (Retrieval-Augmented Generation) & LLM Applications

- Development of domain-specific chatbots and assistants using RAG: document ingestion, chunking, embeddings, and vector search.

- Applied Machine Learning & Deep Learning

- Tabular ML (classification/regression), time-series forecasting, and explainable models for decision support.

- Computer vision solutions for classification, and medical/technical image analysis.

Across all projects, I prioritize clean, reproducible code, clear communication, and delivering solutions that are aligned with your goals, timelines, and technical stack.