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ML Engineer with less than a year in Machine Learning, Computer Vision, and Data Pipelines
Data Science graduate (M.S., University of Texas at Arlington, 2025) with hands-on experience building end-to-end data pipelines, machine learning models, and computer vision systems. Proficient in Python, SQL, PySpark, Great Expectations, and cloud platforms including AWS and GCP. Experienced in data quality engineering, object detection, time-series forecasting, and consumer segmentation. Seeking roles as a Data Analyst, Data Engineer, Data Scientist, or Machine Learning Engineer.
University of Texas at Arlington, USA
Master of Science · Data Science
August 1, 2023 – June 30, 2025
Vellore Institute of Technology, Chennai, India
Bachelor of Technology · Computer Science and Engineering
August 1, 2019 – June 30, 2023
Dezzex Technologies
ML Engineer Intern
June 1, 2026 – Present
Dubai, Dubai, United Arab Emirates
Big Sister Little Sister
Volunteer Data Analyst
November 1, 2025 – Present
India
University of Texas at Arlington
Research Assistant
January 1, 2025 – May 1, 2025
Arlington, Virginia, United States
Cardinality AI
Data Analyst Intern
June 1, 2024 – July 1, 2024
India
GAVS Technologies
Student Intern
August 1, 2022 – September 1, 2022
India
Medicare GX Data Contracts
June 1, 2026 – Present
• Enforced data quality contracts on the CMS Medicare Physician & Other Practitioners 2023 dataset (10M+ provider records) using Great Expectations 1.15.1 across dual execution environments: Pandas (CSV) and PostgreSQL. • Implemented 20+ typed expectations across 7 quality dimensions - schema validation, null checks, type enforcement, NPI format validation, range constraints, categorical state validation, and row-count volume checks. • Built a CI/CD pipeline with GitHub Actions that automatically fails the build on any broken expectation and auto-generates browsable GX Data Docs HTML quality reports on every run.
Data Quality Validation Pipeline
June 1, 2026 – Present
• Built an automated data quality validation pipeline using PyDeequ (AWS Deequ) and PySpark to validate 3-4M rows/month of NYC Yellow Taxi trip records across multiple data quality dimensions. • Implemented 12 constraint checks covering fare amounts, trip distances, timestamps, and passenger counts; performed column profiling and month-over-month drift detection across 3 months of data. • Documented a production-ready AWS Glue migration path, mapping the Colab-based pipeline to an S3-backed metrics repository architecture for billion-row scale deployment.
Quantitative Research Job Simulation
JPMorgan Chase & Co.
January 1, 2026 – Present
Data Analytics Job Simulation
Deloitte
January 1, 2026 – Present
Data Science Job Simulation
British Airways
January 1, 2026 – Present
Introduction to Data for Decision Makers Job Simulation
BCG X
January 1, 2026 – Present
Data Visualisation: Empowering Business with Effective Insights
Tata
January 1, 2024 – Present
Advanced Google Analytics
January 1, 2024 – Present
Google Analytics for Beginners
January 1, 2024 – Present
Power BI - Business Intelligence for Beginners to Advance
Udemy
January 1, 2024 – Present
The Complete SQL Bootcamp – From Zero to Hero
Udemy
January 1, 2024 – Present
MicroStrategy for Business Intelligence
Udemy
January 1, 2024 – Present
Learn Boomi with Sumit Aggarwal
Udemy
January 1, 2024 – Present
Machine Learning
University of Washington
January 1, 2022 – Present
Data Analysis with R Programming
January 1, 2022 – Present
Deep Learning
PadhAl
January 1, 2021 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from Medicare data contracts to NYC Yellow Taxi records and ERCOT grid data, shows a broad interest in applying data science across different domains. Their current ML Engineer Intern role focusing on computer vision aligns well with an ML Engineer target role. The breadth of skills listed, including various programming languages, ML/AI frameworks, data engineering tools, and cloud platforms, indicates a strong willingness to learn and adapt to new technologies. The volunteer data analyst role also suggests a commitment to contributing to organizational growth and accountability.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical and problem-solving skills through their project work, particularly in data quality and time-series analysis. Their experience in volunteer roles and research assistant positions indicates a proactive and collaborative work attitude. The descriptions suggest an ability to manage data lifecycles and establish reporting frameworks, which are valuable for operational fit. However, the resume does not provide explicit details on stress handling or direct team collaboration experiences beyond project contributions.