I design and build production-ready data and AI systems — from scalable ETL pipelines to autonomous LLM-powered applications.

My work focuses on Agentic AI architectures, distributed data engineering, and real-world ML deployment.

🎓 MS Data Analytics/ Applied Data Intelligence — San José State University
⚡ Specializing in AI systems + data platforms
🔬 Interested in intelligent autonomous workflows
💼 Seeking Data Engineering / AI / ML roles

✔ Agentic AI architectures (Planner → Executor → Reviewer systems)
✔ LLM application development & orchestration
✔ FastAPI backend design for ML services
✔ End-to-end data pipelines (batch + streaming)
✔ Cloud data warehousing & analytics engineering
✔ Production ML deployment

• LangChain, LangGraph, Ollama
• Hugging Face Transformers
• Retrieval Augmented Generation (RAG)
• Vector embeddings
• Prompt engineering
• Autonomous agent workflows
• Local & hosted LLM deployment

• FastAPI
• REST API design
• Model serving
• Async processing
• Microservices architecture

• SQL (PostgreSQL, MySQL)
• Snowflake
• Vector databases
• Data warehousing
• ETL / ELT pipelines

• Apache Spark
• Apache Airflow
• dbt
• Kafka streaming
• Distributed systems
• Data pipeline orchestration

• AWS (S3, Glue, Redshift)
• Docker containerization
• CI/CD workflows
• Scalable deployment

• Power BI
• Tableau
• Feature engineering
• Statistical modeling

Autonomous multi-agent architecture using LLM reasoning loops:

• Planner → Task Executor → Validator
• LangChain + local LLM orchestration
• Context memory management
• Structured decision pipelines

Production-ready ML inference service:

• REST API for model prediction
• Hugging Face transformer integration
• Request validation & async processing
• Scalable container deployment

Large-scale data ingestion and transformation system:

• Airflow orchestration
• Spark distributed processing
• Data warehouse integration
• Automated analytics pipeline

Real-time analytics + forecasting:

• API data ingestion
• Feature engineering
• ML prediction models
• Automated dashboards

End-to-end machine learning application:

• Model training pipeline
• FastAPI inference backend
• SQL data storage
• Frontend integration

To engineer intelligent, scalable, and autonomous AI systems that integrate data infrastructure, machine learning, and real-time decision making.

⭐ Always open to collaborating on AI, data engineering, and LLM projects.