ML Infrastructure
Designing systems that are observable, maintainable, and practical for production teams.
Data Engineer
Siva Nekkanti
I build dependable data systems for machine learning, privacy-sensitive workflows, and AI products that need trustworthy infrastructure underneath them.
- Data Platform Strategy
- ML Systems Reliability
- Privacy-Aware Architecture
Focus Areas
My work sits where data engineering, software reliability, and applied AI meet.
Privacy Engineering
Designing systems that are observable, maintainable, and practical for production teams.
Reliable Data Systems
Designing systems that are observable, maintainable, and practical for production teams.
Technical Leadership
I care about systems that make teams faster without making production fragile: clear interfaces, observable pipelines, privacy-aware defaults, and engineering habits that survive scale.
Current Direction
I’m especially drawn to the infrastructure behind AI products: retrieval data, evaluation loops, feature platforms, and reliable feedback systems that turn model experiments into dependable product behavior.