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Vyshnavi

Full-Stack Software Engineer

San Jose, CAvyshnavi@example.comgithub.com/vyshnavilinkedin.com/in/vyshnavi

Summary

Full-stack software engineer with 8 years of experience building production systems at scale. MS candidate in Computer Science at SJSU, focused on distributed systems and adversarial ML research. Shipped 4 significant projects including an AI code reviewer with reproducible evaluation harness and a distributed time-series database from scratch.

Technical Skills

Languages: TypeScript, Python, Go, Java, C#, SQL
Frontend: React, Next.js, Angular, Tailwind CSS
Backend: Spring Boot, FastAPI, Node.js, gRPC
Data: PostgreSQL, Redis, pgvector, Prometheus
AI/ML: PyTorch, LLM/RAG, NLP, Computer Vision, W&B
Infrastructure: Docker, GitHub Actions, Grafana, Raft consensus

Education

MS Computer Science

2024 — 2026

San Jose State University

Focus: Distributed systems, adversarial ML robustness. GPA: 3.9/4.0

BTech Computer Science

2014 — 2018

JNTU Hyderabad

Experience

Software Engineer

2021 — 2024

Accenture — Dell SupportAssist

  • Built real-time telemetry dashboard with Angular + C# + WebSocket for 2M+ Dell devices
  • Reduced diagnostic time by 35% through optimized data pipelines
  • Led migration from monolith to microservices architecture

Software Engineer

2018 — 2021

TCS — Optumera

  • Developed enterprise retail SaaS with Angular + Spring Boot + Redis
  • Implemented virtual scrolling for 100K+ SKU catalogs, cutting render time by 60%
  • Designed and deployed Redis caching layer reducing API latency by 40%

Projects

Sentinel

live

AI code review assistant with reproducible eval harness. F1 metrics per category, regression-gated in CI. Next.js, FastAPI, pgvector, Claude API.

Kairos

live

Multi-tenant OKR platform with Postgres RLS, partitioned audit logs, real-time SSE dashboards. Next.js, Spring Boot, Redis.

Helios

Distributed time-series database from scratch. LSM-tree storage, Raft replication, PromQL queries. Go, gRPC.

NeuroLens

arxiv

Multimodal adversarial robustness toolkit. Novel cross-modal attack with transfer analysis. Published preprint. PyTorch, CLIP.