I build scalable agentic workflows, RAG systems, and distributed training pipelines. Turning research concepts into reliable infrastructure.
I bridge the gap between AI research and production-grade
infrastructure. With a background in distributed GPU training and
genomics sequence classification, I specialize in building the
orchestration layers that make LLMs reliable at scale.
Whether itβs architecting agentic workflows to automate complex
SQL generation or optimizing inference performance for
multi-billion parameter models, my goal is to turn
non-deterministic research concepts into high-availability systems
that save real-world time and cost.
Architected agentic workflows to automate 65% of internal data requests. Rebuilt ETL pipelines reducing runtime by 25% and deployed containerized services on K8s maintaining 99.9% uptime.
Increased genomics throughput by 32% via LoRA/Soft-prompting. Orchestrated 100+ distributed GPU experiments and containerized fine-tuning workflows to slash setup time.
Improved NER F1-score by 8% using proxy-tuned LLaMA 2 models. Optimized inference performance by 30% and reduced training costs by 70%.
Standardized REST APIs reducing integration defects by 40%. Automated CI/CD pipelines via GitHub Actions cutting release failures by 20%.
GPA 3.9/4.0
GPA 8.24/10
Open to roles and collaborations. The fastest way to reach me is email or LinkedIn.