For the past 7 years, I’ve been building high-scale backend systems, data platforms, and GenAI-powered services that process millions of events daily. My expertise lies in Python ecosystems, distributed systems, and cloud-native architecture — with a strong focus on performance, reliability, and infrastructure efficiency.

🔭 I’m currently working on
Designing GenAI-powered backend systems and large-scale event-driven architectures processing 100M+ events/day.

👯 I’m looking to collaborate on
Backend-heavy platforms, distributed systems, high-performance APIs, and production-grade LLM integrations.

🤝 I’m looking for help with
Advanced distributed system patterns, large-scale system design reviews, and performance tuning at extreme scale.

🌱 I’m currently learning
Deeper distributed systems theory, advanced Kafka patterns, and scalable LLM orchestration architectures.

💬 Ask me about
Python backend architecture, Django/FastAPI scaling, AWS optimization, ETL systems, caching strategies, cost reduction, and GenAI in production.

⚡ Fun fact
I treat latency and infrastructure cost like product features — if it’s slow or expensive, it’s a bug.

Always building systems that move real business metrics.

Clarity before cleverness
Scale intentionally — backed by data, not assumptions
Performance and infrastructure cost are product features
Observability is designed in from day one
Reliability is engineered, not patched
Simplicity outlives abstraction

High-scale backend architecture
Production-grade GenAI systems
Distributed and event-driven data platforms
Performance & latency optimization
Cost-aware cloud and infrastructure design