Raahul Sallagunta

About

Application-focused full-stack engineer in the clinical/health domain who shipped an LLM-powered clinical note summarization workflow end-to-end (FastAPI + Postgres + Kafka workers + React/TypeScript UI) with strong attention to security, auditability, and clinician trust. Has hands-on AWS/EKS operations experience and has resolved real production latency/scaling issues through async processing, query/index tuning, caching, and horizontal scaling.

Experience

Education

Key Strengths

• Shipped end-to-end LLM-powered clinical note summarization feature (backend, data model, and UI)
• Designs secure APIs with OAuth2/JWT and role-based authorization for sensitive clinical data
• Builds reliable async architectures (Kafka/workers) to handle long-running processing without blocking UI
• Improves LLM output consistency via structured prompts, JSON validation, and post-processing checks
• Strong product-focused application engineering (API contracts, edge cases, clinician-facing UX)
• PostgreSQL schema design and performance tuning (indexes, composite indexes, EXPLAIN-driven optimization)
• Production AWS/EKS operations with CI/CD and observability; handled latency/scaling incident end-to-end
• Evolved synchronous LLM summarization APIs into async job-based architecture to reduce timeouts and latency spikes
• Designed reliable async processing with idempotent job IDs, retry-safe consumers, and explicit job state transitions
• Improved API responsiveness via Redis caching and reduced key API response times by ~28%
• Strong security implementation for sensitive clinical data (OAuth2/JWT + server-side RBAC, auditable access patterns)
• Proactive observability improvements (correlation IDs, queue depth/lag metrics) to detect hidden async failure modes early
• Incremental/strangler-style migration approach with backward-compatible contracts and gradual traffic rollout