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Fullstack Engineer - AI with 4+ years in AI-Driven Web Applications & Cloud Development
Full-Stack Engineer with 4+ years of experience building scalable, production-grade web applications across the complete stack: React.js, Vue.js, and Angular on the frontend; Node.js and Express.js on the backend; PostgreSQL and MongoDB for data persistence; and AWS for cloud deployment. Combines strong full-stack fundamentals with hands-on AI-driven development, actively using GitHub Copilot, Cursor, Claude Code, Devin, Agentic AI workflows, and MCP Servers to ship higher-quality features faster. Experienced in integrating LLMs and GenAI tools (LangChain.js, Ollama, RAG pipelines) into production applications. Proven impact: 98% load time reduction (17s to 250ms), 60% performance improvement on enterprise SPAs, and 78%+ unit test coverage. Known for clean architecture, reusable component systems, and mentoring developers.
Shri Vaishnav Vidyapeeth Vishwavidyalaya
B.Tech · Information Technology
August 1, 2018 – June 30, 2022
Infosys Ltd
Associate Consultant, Full-Stack Engineer
November 1, 2024 – Present
Pune, Maharashtra, India
Rakuten Symphony
Associate Software Engineer
January 1, 2022 – October 1, 2024
Indore, Madhya Pradesh, India
Internal Knowledge Assistant (RAG Pipeline)
June 1, 2026 – June 1, 2026
Built a full-stack RAG app: React.js frontend + Node.js/Express.js API, storing vector embeddings in MongoDB for semantic document retrieval using local LLMs via Ollama. Designed LangChain.js pipeline: document chunking, embedding generation, vector similarity search, and LLM response synthesis. Evaluated output quality, identified hallucinations and retrieval failures, and refined prompts and chunking strategies to improve accuracy.
AI-Powered Code Review Assistant
June 1, 2026 – June 1, 2026
Built a Node.js tool using LangChain.js and OpenAI API that analyzes React/TypeScript components and returns structured feedback on structure, performance, and edge-case coverage. Applied chain-of-thought prompting and few-shot examples to generate senior-developer-quality, actionable review feedback consistently. Identified AI-generated code with logic errors and missing validations, establishing a human-in-the-loop review standard for AI-assisted workflows.
AI Fluency: Framework & Foundations
Anthropic
June 1, 2026 – Present
Build LLM Apps with LangChain.js
DeepLearning.AI
June 1, 2026 – Present
JavaScript RAG Web Apps with LlamaIndex
DeepLearning.AI
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from traditional full-stack development to advanced AI/GenAI applications, indicates adaptability and a proactive learning mindset. Their experience in migrating legacy systems and optimizing performance aligns with a results-oriented culture. The certifications in AI/LLM development further demonstrate a commitment to continuous learning and staying current with industry trends, which is a strong cultural fit for an innovative environment.
Soft Skills & Operational Fit
The candidate demonstrates strong operational fit through their experience in leading code reviews, sprint planning, and mentoring, indicating good teamwork and leadership potential. Their focus on performance optimization, test coverage, and clean architecture suggests a commitment to quality and best practices. The explicit mention of validating AI-generated code highlights a responsible approach to AI-assisted development.