Software Development Engineer 2 @ Microsoft | Ex- Amazon, Ex- Razorpay
Distributed Systems • Cloud Infrastructure • High-Throughput Ingestion • Search Infrastructure • Cost Engineering

I design and operate distributed systems that sustain high-throughput workloads under unpredictable traffic while maintaining availability and cost efficiency.

Currently working as an SDE 2 (L62) at Microsoft, building scalable cloud infrastructure and services. Previously part of the indexing charter for Amazon OpenSearch Serverless (AWS) — building infrastructure powering vector search and large-scale search workloads.

I focus on:

• Throughput-aware scaling
• Backpressure & flow control
• Observability-first architecture
• Failure-mode resilience
• Cost predictability at scale

• Building scalable cloud infrastructure and services at Microsoft
• Applying distributed systems expertise to large-scale product engineering
• Driving system design, performance optimization, and reliability improvements

Scalability & Control

• Designed and implemented internal Index Rollover API enabling ingestion auto-scaling
• Built controlled backpressure mechanism → reduced availability drops by 90%
• Led 5TB+ ingestion benchmarking to identify system bottlenecks

Observability & Reliability

• Designed telemetry for ingestion, shard recovery, flush & refresh metrics
• Built dashboards for vector search indices (LLM embedding workloads)
• Improved signal-to-noise ratio in operational alerts

Business & Customer Impact

• Resolved 100+ high-severity performance escalations
• Onboarded major enterprise customer → +$2M/month revenue
• Mitigated cost anomaly → $8.7M reduction in customer billing

• Designed backend for high-throughput tax payment workflows → ₹6.4 Crore launch month revenue
• Owned payout-links microservice as SME
• Fixed state inconsistency bug preventing ₹4 Crore financial exposure

Core

• Java
• Golang
• C#
• .NET
• Distributed system design
• AWS & Azure infrastructure

Secondary

• C++
• Python
• Docker
• Kubernetes
• GraphQL

• Flow control algorithms in ingestion-heavy systems
• Vector indexing for large-scale embedding workloads
• Cost-aware distributed architecture
• Lucene internals & indexing pipelines
• Benchmarking & load simulation frameworks

• CodeChef — 2050 (5★)
• Codeforces — 1618 (Expert)
• Google Code Jam Round 2
• Facebook Hacker Cup Round 1

Strong algorithmic foundation complements production systems work.

  B.E., Birla Institute of Technology & Science, Pilani (2020)

Design for failure.
Measure before optimizing.
Scale deliberately — not accidentally.

Impact-driven distributed systems engineering.