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Laukik Waikar

@laukikwaikar

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Full Stack Developer Intern at MCAD Solution who cut hallucinations in a Gemini resume analysis pipeline by 30%.

India

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At MCAD Solution, I implemented prompt guardrails and structured JSON schema validation for a Gemini-based resume analysis pipeline, cutting its LLM hallucination rate by 30%. I also developed an async Node.js backend that increased candidate evaluation throughput by 40%.

At Dev Innovations Labs, I developed reusable TypeScript components for a real-time HR analytics tool and improved application reliability by 35%. I also used Redis caching to reduce redundant session queries by 70% and WebSockets to deliver real-time notifications and messaging.

For CentFluence, an AI-powered personal finance platform, I built a RAG pipeline that categorized transactions with 94%+ accuracy across 10,000+ real transactions. My machine learning research on neurological healthcare datasets achieved 97.7% detection accuracy, and I presented the work at IEEE ICEI 2026.

Experience

Work history, roles, and key accomplishments

Full Stack Developer Intern

MCAD Solution

Jan 2026 - Apr 2026 (3 months)

Cut LLM hallucination rate by 30% in a production Gemini-based resume analysis pipeline by implementing prompt guardrails and structured JSON schema validation. Increased candidate evaluation throughput by 40% by architecting a Node.js backend with async request handling, deployed across Vercel, Render, and MongoDB Atlas.

NodeJS Gemini JSON schema Vercel Render MongoDB Atlas Async Await Prompt Engineering

React Native Developer Intern

Dev Innovations Labs

Dec 2025 - Mar 2026 (3 months)

Improved application reliability by 35% for a real-time HR analytics tool by building reusable TypeScript components with structured error handling. Reduced repeated database load by caching session data in Redis, cutting redundant auth/session queries by 70%. Delivered real-time notifications and messaging using WebSockets, replacing polling-based updates for live data sync by 60%.

React Native TypeScript Redis WebSockets Error Handling Session Management

AI/ML Research Intern

IEEE EMBS Pune

Jun 2025 - Jul 2025 (1 month)

Achieved 97.7% detection accuracy on neurological healthcare datasets by benchmarking 5 ML algorithms, including BiLSTM, against F1-score, precision, and AUC-ROC. Improved experiment reproducibility through version-controlled experiment tracking and standardized ML workflow documentation.

Machine Learning Bi LSTM F1 Score Precision AUC ROC Experiment Tracking Version Control

Education

Degrees, certifications, and relevant coursework

Marathwada Mitra Mandal’s College of Engineering, Pune

Bachelor of Engineering, Computer Engineering

2023 -

Grade: 9.21/10.00

Activities and societies: LeetCode: 320+ problems solved; Maximum Contest Rating: 1485.

Pursuing a Bachelor of Engineering in Computer Engineering with a minor in Data Science & Visualization, Statistics, and Machine Learning. Achieved a CGPA of 9.21/10.00.