I design and ship production-scale AI systems — from high-throughput inference engines to multi-agent orchestration frameworks. Focused on the intersection of systems engineering and frontier AI research.
High-performance inference backbone serving heterogeneous LLM deployments. Optimized for maximum throughput with dynamic quantization, speculative decoding, and intelligent KV cache management across diverse model architectures.
Multi-agent orchestration framework for autonomous task solving. Features hierarchical reasoning with stateful agent collaboration, robust guardrails, and persistent state management for complex, long-horizon enterprise workflows.
Kubernetes-native AI platform toolkit for multi-tenant inference clusters. Provides unified request routing, zero-trust service mesh, and automated GPU workload scaling with CLI-driven lifecycle management.
Regular deep-dives into frontier research — from infinite-context architectures and memory-efficient training to RLHF alignment. Each paper reading includes detailed presentations with implementation insights.
Intelligent Q&A system for clinical trial documents featuring named entity recognition for adverse drug events, Haystack-based retrieval, and an LLM-powered dashboard for interactive exploration of medical literature.
Predictive analytics pipeline for pharmaceutical demand planning using ensemble time-series methods. Achieved MAPE under 20% by combining ARIMA, Holt-Winters exponential smoothing, and XGBoost regressors.
Real-time anomaly detection system for surveillance video streams, achieving 95% precision on the UCSD Anomaly Detection Dataset. Combines spatial-temporal feature extraction with unsupervised learning.
"The future of AI isn't just smarter models — it's smarter systems. I believe the most impactful work happens at the intersection of rigorous engineering and frontier research, where ideas are stress-tested against production reality."
Enabling infinite-context inference without KV cache explosion
Neuro-symbolic system combining language models with symbolic deduction
Compressive memory for infinite context length in transformers
Reducing fine-tuning memory footprint via gradient projection
Improving reward stability and alignment in RLHF fine-tuning
Layer-wise adaptive rate scaling and large-batch convergence