Data engineering
SQL Server, T-SQL, SSIS, ETL, data warehousing, data quality, and pipeline design.
About the work
Practical AI, grounded in enterprise data.
I am an AI / GenAI Engineer with a strong foundation in data engineering and business intelligence. With 6+ years of experience across SQL, ETL, SSIS, SSRS, Power BI, Python, and data science, I build AI applications that can work with real enterprise data and operational workflows.
My current work spans machine learning, LLM applications, RAG, AI agents, tool calling, MCP, FastAPI, and production-oriented AI systems. The data-engineering background matters: reliable context, clear interfaces, validation, and operational constraints are part of the AI problem.
Current focus
From data foundations to agentic systems
Generative AIRAG SystemsAI AgentsTool CallingMCPLLM ApplicationsAI + Data EngineeringProduction AI
Engineering philosophy
Build practical systems, not demos.
Use deterministic logic where it is appropriate, LLMs where semantic reasoning is needed, and structured outputs everywhere the system needs to stay predictable. Validation, evaluation, observability, security, and production readiness are design concerns, not afterthoughts.
Experience areas
A connected toolkit
Analytics & ML
Power BI, DAX, Power Query, Python, Pandas, NumPy, machine learning, and deep learning foundations.
AI application engineering
RAG, tool calling, agents, MCP, FastAPI, Streamlit, APIs, testing, logging, and evaluation.