Sai Anuhya Bandi

About

Full-stack engineer with 5+ years of experience who built real-time data visualization and analytics systems at Uber, spanning React/TypeScript frontends, Node/GraphQL services, Kafka pipelines, and PostgreSQL. Particularly compelling for teams needing a hands-on builder who can turn ambiguous customer needs into scalable products, and who has also applied RAG with LangChain/OpenAI over 1.8M support files to surface actionable insights.

Key Strengths

• end-to-end full-stack ownership
• real-time data systems and visualization expertise
• strong TypeScript/React/Node.js/GraphQL stack
• scalable backend architecture design
• hands-on builder mentality with 80%+ coding time
• customer-driven product development
• ambiguity reduction through stakeholder collaboration
• performance optimization across frontend and backend
• testing and maintainability focus
• AI-assisted analytics and RAG implementation
• high-throughput service development
• data-driven post-launch iteration
• Built enterprise LLM assistant (AI Genie) using LangChain + GPT-4 + Pinecone RAG for broadband analytics insights
• Designed and operated ETL pipelines processing 10M+ records daily
• Improved data freshness by 60% through pipeline and processing optimizations
• Backend scalability patterns: modular Flask architecture, async workers to prevent API blocking
• Production deployment scalability via containerization and Kubernetes (horizontal scaling, rolling updates, minimal downtime)
• Database performance optimization with SQLAlchemy/PostgreSQL (indexing, connection pooling, bulk ops, avoiding N+1 queries)
• Multi-tenant design with strong data isolation (schemas/tenant IDs), access controls, encryption, and quotas/rate limits
• Integrated AI/ML workflows using microservices and event-driven processing across cloud-native stack (Kubernetes/Lambda)
• Built and operated production Python backend services for LLM/workflow-driven analytics (FastAPI/Flask) on AWS/GCP with Docker/Kubernetes
• Debugged and resolved P95 latency spikes/timeouts by addressing PostgreSQL connection/write pressure, indexing, batching writes, and adding bounded async concurrency/backpressure
• Strong production-grade AI/LLM guardrails: RAG from approved sources, structured JSON + schema validation, and post-generation verification against databases to prevent hallucinations
• Designed reliable multi-step agent systems with clear planning vs deterministic execution, tool timeouts, idempotent retries, and stop/escalation logic
• Improved maintainability and reliability via typing (MyPy), Pydantic schemas, modular boundaries, and tests (unit/contract/integration); fixed async race condition by removing shared mutable state
• Relational data modeling for workflow systems (runs/steps/artifacts) with constraints for idempotency and performance-focused indexing
• Built and deployed production LLM/RAG analytics platform for executive-level broadband analytics
• Scaled data processing via ETL pipeline handling 10M+ records daily
• Improved LLM reliability at scale via grounding (schema-aware prompts, metadata filtering) and retrieval tuning (chunking)
• Reduced latency through caching and parallel retrieval
• Strong evaluation/monitoring approach using golden datasets and production metrics (correctness, latency, retrieval precision, fallback rates)
• Effective collaboration with non-technical stakeholders through prototyping, validation, and iterative delivery

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