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Viswanath Jagaluri

Mid-level Full-Stack & AI Engineer specializing in LLM applications

Software Engineer / Full Stack Developer6 years experienceMid-LevelTechnologySaaSWeb Development
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About

Full-stack engineer who has shipped and operated generative-AI chat/QA features end-to-end, including a RAG-based pipeline with guardrails and cost/latency monitoring in production. Experienced with React/TypeScript + Node/Postgres architectures, Dockerized deployments to AWS (EC2) via GitHub Actions CI/CD, and building reliable ingestion/ETL systems with idempotency, backfills, and reconciliation.

Experience

Software Engineer / Full Stack DeveloperOur National Conversation
Digital Specialist Engineer (Full Stack Developer)Infosys
Systems Engineer (Full Stack / Frontend Engineer)Appwrk IT Solutions

Education

Fitchburg State University, Massachusettsmaster, Computer Science
Lovely Professional University, Punjab, Indiabachelor, Computer Science and Engineering
Fitchburg State Universitymaster, Computer Science

Key Strengths

  • Owned end-to-end delivery of a generative-AI chatbot feature (frontend, backend, deployment/ops)
  • Built and operated RAG pipeline with production guardrails (prompt tuning, validation, caching/batching) to reduce latency and hallucinations
  • Strong production observability mindset (structured logs, latency/error KPIs, LLM cost tracking, alerting)
  • Designed scalable full-stack architecture (modular React/TS components; thin controllers + service layer; standardized validation/error handling)
  • Reliable data ingestion/ETL engineering (idempotent upserts, retries with backoff, circuit breakers, checkpoints/backfills, reconciliation)
  • Shipped end-to-end RAG Q&A feature to production (ingestion → embeddings → FAISS search → LLM answers via FastAPI)
  • Reduced hallucinations ~70% via retrieval improvements and strict context grounding
  • Cut latency ~20% while maintaining 99.9% uptime in production
  • Scaled services to thousands of concurrent users using containerization, horizontal scaling, load balancing, and Celery offloading
  • Strong observability and reliability practices (Prometheus/Grafana, structured logging, alerting, Sentry, staged/rolling rollouts)
  • Performance optimization under load: refactored a timing-out endpoint to async Celery + Redis caching + batched DB queries (5s → <500ms)
  • PostgreSQL performance tuning at scale (composite/partial indexes, materialized views) achieving common query times <200ms on millions of events
  • Handled real AWS production incident end-to-end (traffic spike causing 502s): scaled ASG, added PgBouncer, fixed slow query via Performance Insights

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Languages

English

Skills

PythonJavaJavaScriptTypeScriptSQLC#.NETCC++ReactAngularReduxNgRxRxJSTailwind CSS