I am an AI/ML Engineer with expertise in MLOps, distributed training, and scalable infrastructure, delivering production-grade ML systems across cybersecurity, financial services, network intelligence, and analytics. My work spans the entire ML pipeline — from data engineering and model development to deployment and lifecycle management — supported by patents, publications, mentorship.
I believe AI must be responsible, resilient, and sustainable. I design robust, adaptive, climate-aware infrastructure that aligns cutting-edge AI with long-term efficiency and reliability - minimizing compute cycles, optimizing power consumption, and reducing overhead without compromising scalability or performance. I embed sustainability into AI engineering to enable trustworthy, energy-efficient innovation that grows responsibly with compute demands.
Beyond industry, I co-founded Circassian DNA, a non-profit initiative leveraging AI-driven genealogy and genetics to reconnect the Circassian diaspora. We uncover shared ancestry, reinforce cultural identity, and rebuild connection across generations, geographies. I see the power of AI not only to solve complex challenges but also to foster human connection, preserve cultural heritage, and drive meaningful impact.
Python, Rust, SQL, Bash
Docker, Kubernetes, Kubeflow, MLFlow, FastAPI, Flask, gRPC, AWS, GitOps, ArgoCD, Helm
XGBoost, LightGBM, Ray, Scikit-learn, ONNX, KServe, TorchServe, TensorRT
PyTorch, TensorFlow, Keras, Transformers, LSTM, GRU, GNN, PyG, NetworkX
LLM, vLLM, LangChain, LlamaIndex, RAG, Pinecone, Hugging Face, Fine-tuning
Pandas, NumPy, SciPy, Matplotlib, Seaborn, Plotly, Bokeh, JupyterLab