DEDEEPYA PALAKURTHI

About

Backend engineer who built an AI-driven "Smart Feedback Analyzer" API (Flask → FastAPI) that processes user feedback with NLP (Hugging Face + OpenAI) and returns structured insights. Demonstrates strong production-minded architecture: stateless services, Cloud Run + Docker deployment, Redis/Celery background processing, and Postgres/SQLAlchemy performance tuning (EXPLAIN ANALYZE, indexing, N+1 fixes), plus multi-tenant data isolation via JWT/API-key derived tenant IDs.

Experience

Education

Key Strengths

• Designed scalable stateless APIs with Redis/Firestore for temporary state
• Improved API responsiveness by offloading long-running NLP to background workers (Celery/Redis)
• Strong SQLAlchemy/Postgres performance tuning (indexes, EXPLAIN ANALYZE, joinedload to fix N+1)
• Implemented multi-tenant isolation using org_id derived from JWT/API keys and enforced tenant filtering
• Operationalized AI workflows (Hugging Face + OpenAI) with retries/backoff, output normalization, and cost controls via caching
• Reduced API response time from several seconds to under 200ms via caching + background processing
• Built end-to-end full-stack AI feedback analytics app (React/TS + FastAPI + Postgres) deployed on GCP with Kubernetes
• Performance/scalability optimization in Kubernetes (lazy model loading, HPA, caching) to reduce latency and improve stability
• Large-scale data processing and ML inference workflows (tens of thousands of aviation records) with batch processing and optimized DB queries
• Strong API design in microservices (clear ownership, contract design, auth, data modeling, service interactions)
• Pragmatic React state management (local state/custom hooks, Context, React Query; Redux Toolkit when needed)
• Effective use of AI coding tools with attention to code review and security (no sensitive data leakage)