I'm a Senior Data Scientist & AI Engineer with 6+ years of experience designing, deploying, and scaling production-grade AI systems in enterprise environments. I have a background in Electrical Engineering, a Master’s degree in Engineering, and I am currently completing a PhD in Engineering (Data Science).
My work sits at the intersection of applied research and AI engineering, with a strong focus on non-stationary time-series modeling, intelligent agents, and end-to-end AI systems.
My research activity is centered on machine learning for non-stationary environments, where data distributions evolve over time and models must remain reliable under uncertainty.
Topic: Non-stationary time-series forecasting using machine learning
Research focus:
- Learning under distribution shift and regime changes
- Robust multi-horizon forecasting strategies
- Generalization across datasets, regions, and temporal scales
- Bridging statistical learning theory and deep learning
Key contributions:
- Design of ML and DL frameworks for complex temporal dynamics.
- Integration of kernel methods, neural architectures, and adaptive learning strategies.
- Emphasis on robustness, interpretability, and stability, beyond point-wise accuracy.
- Experimental protocols aligned with real-world decision-making constraints.
This research directly informs how I design forecasting systems, AI agents, and data-driven products, ensuring models remain dependable in dynamic, real-world conditions.
I build end-to-end AI systems that move seamlessly from research to production.
- Design and deployment of Retrieval-Augmented Generation (RAG) systems using LangChain, LLMs, and vector databases.
- Enterprise conversational agents with long-term memory (MongoDB) and secure data access (Snowflake).
- Cloud-native and serverless architectures for scalable, low-latency AI services.
- Advanced time-series modeling informed by research on non-stationarity.
- Classical ML and statistical modeling for structured and unstructured data.
- Feature engineering, evaluation strategies, and model governance.
- Deep learning models for NLP, computer vision, and audio/video analytics.
- Experience with PyTorch and TensorFlow, including custom components.
- Focus on generalization, stability, and interpretability.
- AI deployment using Docker, FastAPI, and serverless pipelines.
- Cloud experience across AWS and Azure.
- Practical focus on scalability, cost-efficiency, observability, and governance.
- AI systems must remain robust under uncertainty and change.
- Research is valuable only when it translates into production impact.
- Models should be interpretable, testable, and maintainable, not just accurate.
Languages & Core
- Python · Linux · SQL
Machine Learning & AI
- PyTorch · TensorFlow · scikit-learn
- Time-Series Forecasting · NLP · Computer Vision
- Statistical Learning & Model Evaluation
Agents & LLM Tooling
- LangChain · Vector Databases (Chroma)
- Retrieval-Augmented Generation (RAG)
- Memory & Context Management
Cloud & Deployment
- AWS · Azure · Docker · FastAPI
- Serverless Architectures · MLOps Foundations
- Multi-agent systems and agent orchestration
- Advanced MLOps, observability, and evaluation
- Reliable AI systems under distribution shift
