I am a Data Scientist and Machine Learning Engineer with 3+ years of experience building end-to-end data and AI systems that move beyond experimentation into production. My background spans applied statistics, scalable data engineering, and modern machine learning, with a strong focus on delivering measurable business outcomes.

I have designed and deployed forecasting, analytics, and automation pipelines using Snowflake, Azure, and cloud-native tooling, driving up to 60 percent efficiency gains and 25 percent accuracy improvements across large-scale, real-world datasets. I enjoy working at the intersection of data, engineering, and stakeholders to turn complex problems into reliable systems.

Currently, I specialize in Generative AI, LLMs, and MLOps, building high-performance inference engines, RAG-based applications, and OpenAI-compatible APIs with streaming, observability, and production-grade constraints. I believe strong models matter, but robust systems, scalability, and trust matter more.

Outside of work, I contribute to open-source projects, experiment with new AI architectures, and write about applied machine learning, system design, and responsible AI practices.