Khushleen Jaggi

About Khushleen Jaggi

I’m a Software Engineer with 1 year of industry experience, including hands-on work in full-stack development, data analysis, and AI-powered tools. Over the past year, I’ve contributed to real-world projects in backend systems, automation, and speech-to-text integration, with a strong focus on delivering accurate, scalable solutions. Technical Skills Skilled in Python, Java, JavaScript, React.js, Node.js, SQL, MongoDB, Google Apps Script, and Whisper. I’ve also worked with AWS Bedrock and tools for automating and validating streaming data workflows. Experience & Impact At Nielsen, I worked on the Smart Streaming Crediting project, validating large-scale streaming data, integrating Whisper-based transcription into production code, and automating reporting workflows. I’ve also collaborated with cross-functional teams to optimize data pipelines and support ML development efforts. Career Goals I’m eager to build a strong career in software and data engineering, where I can contribute to impactful, data-driven products. I enjoy problem-solving, working across tech stacks, and continuously learning in a fast-paced environment. Open To Full-time opportunities in Software Development, Data Analysis, Data Science, or Machine Learning, where I can grow as a developer and contribute to high-impact solutions.

Experience

1. Software Engineer Intern

   Nielsen

   Jul 2024 — Present · Gurugram, IN

   Engineering R&D – Smart Streaming Crediting• Led the Smart Streaming Crediting project, using Netsight meters to verify ground-truth data across multiple device types (TVs, PCs, Firesticks), ensuring 100% validation accuracy.• Built and optimized a centralized 2024 ground-truth database, improving accessibility and reducing data retrieval time by 30%.• Integrated Python-based speech-to-text processing into LLM workflows using AWS Bedrock, significantly enhancing transcription accuracy and speeding up data analysis.• Tracked false identifications, maintained a structured issue tracker, and calculated error rates to improve the recommendation engine\'s output quality.• Automated validation workflows, reducing manual effort by 40%, and integrated device and model data into dashboards for real-time monitoring and diagnostics.

Education

• Continental Intitute for International Studies

  Diploma of Education

  2019 — 2020

• Dav Public School Ambala City

  10th

  2016 — 2017

• Dav Public School Ambala city

  12th

  2018 — 2019

• Chitkara University

  Bachelor of Engineering - BE

  2021 — 2025