AI Engineer @ Toyota Β· MSDS @ NYU CDS Β· MLE @ OpenHealthcare US
Turning research ideas into production-grade AI products across LLM systems, multimodal AI, evaluation, and cloud MLOps.
- LLM systems: RAG and agent workflows, evaluation and guardrails, retrieval orchestration, and production deployment
- Applied AI: NLP, transformers, multimodal applications, computer vision pipelines, and human-centered products
- ML engineering: MLOps, model serving, observability, cloud-native tooling, and research-to-production delivery
- Shipping practical LLM systems and AI agents for real users
- Going deeper on retrieval, orchestration, evaluation, and observability
- Studying from-scratch model implementations to sharpen fundamentals
- Building AI products across healthcare, enterprise automation, and mobility
mcp-automation-system: Enterprise AI automation for PR review, Asana context, and Slack deliveryMedibot-GraphRAG: Hospital-focused GraphRAG assistant built with LangChain and Neo4jTheramind: Offline mental-health app powered by a fine-tuned small language model
Align2Act: Instruction-tuned models for human-aligned autonomous drivingfashion-recsys: End-to-end recommendation system for fashion retrieval and rankingLLMs-from-Scratch: Hands-on implementations of LLMs, SLMs, and VLMs for experimentation
Languages And Frameworks
ML And AI
MLOps And Cloud
Data, Retrieval, And Systems
If you're building with LLMs, applied ML, or production AI systems, let's connect.



