Vikas Katuru

About

Backend-leaning full-stack engineer who has built AI-powered analytics products from 0→1, including a predictive analytics dashboard and an AI orchestrator for natural-language-to-database querying. Particularly strong in making LLM systems production-safe through schema validation, self-healing retries, monitoring, and retrieval optimization, with quantified impact on cost, latency, and quality.

Key Strengths

• Built a predictive analytics dashboard end-to-end across frontend, backend, and data layer
• Strong backend-for-frontend architecture using strict JSON schema validation to stabilize UI behavior
• Performance optimization across API and database layers with Redis caching
• React component architecture with container/presentational separation and disciplined memoization
• 0→1 builder of an AI orchestrator at a startup
• Reduced API costs by 80%
• Cut manual setup time by 70%
• Improved retrieval quality by 35% through semantic chunking and Cohere rerank
• Reduced latency by 30% using semantic routing
• Built self-healing retry mechanisms for unreliable LLM workflows
• Production monitoring of AI systems using LangFuse with metrics like faithfulness, latency, and reliability
• Testing discipline with 85% unit test coverage in prior work

Reference Highlights

• strong ownership of complex systems
• trusted executor
• secured cloud environment
• delivered reliable AI search capability
• security-first mindset
• raised engineering standards
• teaches team to build safe AI systems
• clean, maintainable code
• highly detailed documentation
• enables team scalability and autonomy
• practical product instincts
• prioritizes safety and reliability
• makes thoughtful tradeoffs
• clear communicator
• sets realistic expectations
• strong follow-through
• translates compliance needs into technical solutions
• proactive problem solving
• flawless execution
• complete ownership of features from scratch
• strong full-stack balance
• builds lightweight frontends
• builds secure and reliable backend services
• delivers working, scalable features
• trustworthy execution
• improves team collaboration
• raises engineering standards
• reduces cross-team debugging
• structured workflow approach
• clear documentation
• pragmatic engineering mindset
• keeps codebase lightweight and maintainable
• ensures frontend stability
• safe refactoring approach
• smart UX tradeoffs
• prioritizes reliability and user trust
• iterates based on real usage
• fast, safe, reliable user experiences
• excellent at translating business needs into systems
• effective communication with non-technical teams
• improved AI accuracy by 35%
• cut manual setup time by 70%
• reduced API costs by 80%

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