Backend engineer specializing in high-performance distributed systems and AI infrastructure. I architect scalable pipelines that process millions of events daily, evaluate production ML models, and build robust APIs serving global user bases. Currently focused on bridging the gap between research-grade AI and production-ready systems.
| Domain | Technologies |
|---|---|
| Languages | Python, C++, JavaScript, SQL, Bash |
| Backend & APIs | FastAPI, Django, Flask, gRPC, REST, Microservices |
| AI/ML Systems | PyTorch, TensorFlow, Hugging Face Transformers, Model Evaluation & Validation Pipelines |
| Data & Messaging | Kafka, Redis, PostgreSQL, ClickHouse, Elasticsearch |
| Infrastructure | Docker, Kubernetes, Terraform, AWS/GCP, CI/CD |
| Observability | Prometheus, Grafana, ELK Stack, Distributed Tracing |
Current Priorities:
- Architecting low-latency inference pipelines for AI deployments
- Building event-driven systems handling millions of events daily
- Establishing MLOps practices: monitoring, versioning, and automated retraining
- Contributing to foundational Python tooling and AI frameworks
I contribute to projects where reliability and developer experience intersect. Recent contributions focus on:
- Performance optimizations in async Python frameworks
- Documentation and testing infrastructure for ML libraries
- Tooling for distributed system observability
"Production code is read 10x more than it's written. I optimize for the next engineer."
Open to collaborations on infrastructure-heavy projects, AI system architecture, and performance engineering challenges.