00 · Index
A compact map of what I build and what I spend time thinking about.
I'm Rakesh Sawant — an AI/ML engineer who builds retrieval systems, GenAI pipelines, and enterprise intelligence that holds up in production.
Most AI systems are impressive in demos. Fewer survive messy data, shifting requirements, and the complexity of real software ecosystems. That gap is where I work.
The work spans synthetic data, Hybrid RAG, knowledge graphs, and semantic retrieval — designed to be reliable, not just functional.
01 · Current Work
Bridging AI research and enterprise engineering.
I'm currently an AI/ML Engineer at Strategy (formerly MicroStrategy), embedding AI capabilities into enterprise business intelligence platforms.
The work sits across retrieval systems, model orchestration, scalable AI services, distributed data workflows, and backend APIs — making ML features behave reliably inside large, complex software ecosystems.
Outside work: system design rabbit holes, Graph-RAG experiments, agent architectures, and a persistent interest in making AI systems more observable than mysterious.
02 · Experience
A shorter version of LinkedIn.
| Years | Company | Focus |
|---|---|---|
| 2025 — now | Strategy (MicroStrategy) | AI/ML Engineer · retrieval systems, model orchestration, scalable AI services, enterprise BI |
| 2023 — 2025 | Cognizant | AI Engineer · Hybrid RAG, synthetic data, knowledge graphs |
| 2022 — 2023 | Cognizant | ML Engineer · semantic search, ML pipelines |
| 2017 — 2021 | SPPU | B.E. Electronics & Telecommunication |
03 · Notes
Things worth writing down.
- The Async AI Processing Pipeline: Decoupling Storage from Intelligence Why treating ML pipelines like HTTP requests will destroy your production system, and how to build AI infrastructure that actually scales.
04 · Reach