AI Engineer & Data Scientist
Building AI agents that automate enterprise workflows (HR, ATS, lead gen) AND deploying production-grade ML/DL models—from research to real-time APIs and offline edge environments. I turn raw data into measurable business impact.
Out[00]: Frankfurt, Germany · Open to remote
I'm an AI engineer and data scientist with a dual focus: building AI-powered agents for enterprise automation (HR, ATS, lead gen) and deploying production-grade ML/DL models across real-time APIs and offline edge environments. I manage the full lifecycle—from raw data pipelines and feature engineering to model training, evaluation, and MLOps. I'm passionate about turning complex business problems into robust, scalable solutions, and I value reproducibility, clear communication, and systems that stay reliable after deployment.
Teaching AI engineering, data science, and ML/DL deployment to 11,000+ students. Focus on bridging the gap between theory and production-ready systems, with an emphasis on industrial automation, model deployment, and intelligent agent development.
Leading the development of AI-powered HR automation systems, including resume screening, ATS services, and HR operations using n8n, python, and custom AI agents. Building scalable agentic services deployed with FastAPI and Docker.
Researching and developing ML/DL models with a focus on Federated Learning and healthcare applications. Building robust end-to-end data pipelines—from ETL and feature engineering to hyperparameter tuning and evaluation. Implementing full MLOps with MLflow for experiment tracking and model versioning.
A comprehensive resume screening and HR automation system that streamlines the entire recruitment workflow. Uses LangChain, Pinecone vector DB, and GPT-4 to parse, analyze, and match resumes against job descriptions with 92% accuracy, cutting HR screening time by 60%. Features automated candidate ranking, skill extraction, and interview scheduling.
View project → : AI Recruiter — Resume Screening & HR Automation
A hybrid forecasting system combining Gradient Boosting and LSTM neural networks for multi-horizon demand prediction. Deployed as a microservice with Docker and FastAPI, featuring auto-retraining pipelines and real-time monitoring via MLflow.
View project → : Demand Forecasting Engine
A retrieval-augmented generation (RAG) system that allows companies to upload their internal documents (PDFs, Word, markdown) and query them using natural language. Built with Qdrant vector database and deployed on a custom VPS. Features custom embedding pipelines, efficient chunking strategies, hybrid search, and conversation memory for contextual follow-ups.
View project → : Enterprise RAG — Document Q&A SystemHave a project in mind, or just want to talk about AI? My inbox is open.