Federated Learning with Blockchain & IPFS
A decentralized, auditable, and transparent AI training ecosystem with agentic AI monitoring.
AGENTIC AI SYSTEMS · CONTEXT ENGINEERING · PRODUCTION RELIABILITY
I design and build intelligent AI agents and machine learning systems that work reliably in production.
I specialize in agentic AI architecture, context engineering, evaluation frameworks, and systems that reason, plan, and act across multi-step workflows—with a focus on measurable impact and responsible AI practices.
0Production models
0% Model uptime
0Client programs
0Years in field
Selected Projects
Detailed implementation work with practical engineering decisions and production constraints.
ML Audit Library
Solves Data Lineage Blindness by tracking granular preprocessing steps.
Applied Research
Research projects with the same end goal: real-world robustness, measurable value, and clear deployment pathways.
Fair and Explainable Credit-Scoring under Concept Drift
Adaptive Explanation Frameworks for Evolving Populations
Research on Adversarial ML in Cybersecurity
Exploring the use of adversarial machine learning for enhancing cybersecurity.
Tech Stack
Tools I use to ship and maintain ML systems in real environments.
Languages: C · Python · C++ · R · Solidity
MLOps & Deployment: Kubernetes · MLflow · Docker · Apache Airflow · DVC · Azure ML / AWS · Flask / FastAPI
Libraries: TensorFlow · PyTorch · Scikit-learn · Hugging Face Transformers
AI & Agentic Systems: LangChain · LlamaIndex · Anthropic API · CrewAI · Prompt Engineering · Vector Databases (Chroma, FAISS) · Embedding Models
Data & Infra: Git · MongoDB · MySQL · Redis · Snowflake · Linux
Delivery: Web development · Web scraping · Python + Gen AI automation · RAG Systems · Evals & Monitoring
Let’s build dependable ML products.
If you need machine learning that is designed for long-term use, I’d love to collaborate.