Engineering high-throughput, low-latency machine learning systems, distributed training frameworks, and reliable cloud-native infrastructure.
Systems background optimized for large-scale AI deployment.
I am a Software Engineer pursuing my Master's in Software Engineering at San Jose State University. My technical core is focused at the intersection of Systems Programming, Distributed Platforms, and Machine Learning Operations (MLOps).
I specialize in maximizing compute efficiency, managing GPU resources for heterogeneous environments, and building robust cloud-native systems processing multi-terabyte data workflows.
My goal is to advance the state-of-the-art in Big Model-as-a-Service (BMaaS) platforms, reducing the time-to-market for NLP, GenAI, and large-scale deep learning models worldwide.
Explore my open-source software projects and active telemetry simulations.
High-performance GPU inference benchmarking tool for NVIDIA Triton Server with automated latency profiling and hardware telemetry.
Autonomous, state-persistent recovery agent for stranded travelers. Implements Vector search and stateful orchestration graph workflows.
Distributed data platform processing 5TB+ daily with 100+ concurrent jobs. Employs PySpark, Airflow scheduler, and Kubernetes pods.
Cloud-native, event-driven order processing engine. Implements distributed idempotency keys, optimistic locking, and message queue patterns.
End-to-end real-time ingestion pipeline processing stock market data streams using messaging clusters and validation triggers.
Reach out for collaborations, system designs, or technical inquiries.