Detail-oriented Quality Analyst & Software Quality Assurance Engineer specializing in AI output evaluation, data validation pipelines, and comprehensive end-to-end QA automation.

B.Tech in Mathematics & Data Science @ MANIT Bhopal (2022 – 2026)
Experienced in structural verification, API testing, regression-style AI output analysis, and building reliable CI/CD pipelines.

LinkedIn • GitHub

• High-Scale Test Suites: Implementing automated End-to-End (E2E) testing patterns (Playwright) and API validations (Postman).
• AI & Data Integrity: Structured quality evaluation of AI models, code generation correctness, and complex data pipeline accuracy.
• Fault-Tolerant Automation: Designing automated event tracking, error logging, and system monitoring tasks.
• Clean Architecture & CI/CD: Ensuring production-grade technical execution with strict linting, type systems, and Git protection gates.

A high-fidelity B2B SaaS platform for outbound RevOps. Architected and implemented from the ground up as a solo creator.

• Developed the monorepo architecture (Next.js, TypeScript, PostgreSQL, Prisma, BullMQ, Redis).
• Designed and wrote the full automated Playwright test suite for critical integration workflows, user sessions, and database constraints.
• Integrated automated security scanners (Gitleaks) and dependency auditing to enforce strict CI/CD gates.

A Python-based data validation and machine learning pipeline to detect fraudulent transactions.

• Preprocessed, cleaned, and validated large-scale transaction datasets using Pandas to catch edge cases.
• Built and tested classification models (Random Forest, KNN) using systematic performance metrics (precision/recall, confusion matrix).

A fault-tolerant automated pipeline to monitor real-time data ingestion across APIs.

• Built using n8n and Python scripts to validate request/response integrity and timeout scenarios.
• Configured real-time Slack hooks for failure alerts and zero-data-loss validation.

• QA & Automation: Playwright, Selenium, Appium, Pytest, Postman (API Testing)
• Languages: Python (Pandas, Scikit-learn), TypeScript, JavaScript, SQL (MySQL, PostgreSQL)
• DevOps & Databases: GitHub Actions (CI/CD), Supabase, n8n, AWS (Basic), Tableau
• Testing Methods: Smoke, Sanity, Regression, Integration, E2E, Defect Life Cycle

• Forensic Data Analysis & Validation — Deloitte Data Analytics Virtual Simulation (Dec 2025)
• Data Science & Analytics — 365 Careers Bootcamp (Jun 2025)

_{Driven by technical precision, software quality discipline, and building robust, automated software systems.}