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AI/ML Engineer with less than a year in end-to-end data pipelines, deep learning model development,
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Detail-oriented Data Analyst and Machine Learning Engineer with hands-on experience in end-to-end data pipelines, deep learning model development, and computer vision applications. Skilled in building CNN-based classification systems for medical imaging (pneumonia detection, disease classification) and AI-generated image detection, as well as classical ML for predictive analytics. Proficient in Python, TensorFlow/Keras, SQL, and Power BI. Microsoft Certified in Data Analytics.
Thapar Institute of Engineering and Technology, Patiala
M.Tech · Computer Science & Engineering
August 1, 2025 – June 30, 2027
Modern Institute of Technology and Research Centre, Alwar
B.Tech · Computer Science & Engineering
August 1, 2020 – June 30, 2024
Saint Farid Public School, Mandi Gobindgarh
12th Non-Medical · CBSE Board
June 1, 2019 – May 31, 2020
Accenture (Virtual)
Web Analytics Job Simulation
February 1, 2024 – February 29, 2024
India
Deloitte (Virtual)
Data Analytics Job Simulation
January 1, 2024 – January 31, 2024
India
Mango Leaf Disease Detection | Deep Learning Image Classification Project
January 1, 2024 – December 31, 2024
Built and trained a CNN-based deep learning model to classify mango leaf diseases from image datasets, achieving high accuracy across multiple disease categories. Applied transfer learning using pre-trained models (VGG16/ResNet) with fine-tuning to improve classification performance on limited agricultural image data. Executed image preprocessing pipeline including resizing, normalization, and augmentation (flipping, rotation, zoom) to enhance model generalization and robustness. Evaluated model using accuracy, precision, recall, F1-score, and confusion matrix; documented all results and anomalies with status reports for iterative improvement.
Pneumonia Detection from Chest X-Rays | Medical Imaging Classification Project
January 1, 2024 – December 31, 2024
Developed a binary image classification model to detect pneumonia from chest X-ray images using deep learning, trained on labeled medical imaging datasets (Normal vs. Pneumonia). Implemented transfer learning with InceptionV3/VGG16 and applied class weighting to handle dataset imbalance, improving recall for the positive (pneumonia) class. Built end-to-end pipeline covering image ingestion, preprocessing, augmentation, model training, and threshold tuning aligned with clinical sensitivity requirements. Reported model performance metrics including AUC-ROC, sensitivity, and specificity; documented data quality issues and maintained validation standards throughout execution.
Fake Image Detection | AI-Generated vs. Real Image Classification Project
January 1, 2024 – December 31, 2024
Built a deep learning classifier to distinguish AI-generated/synthetic images from authentic real images, addressing the growing challenge of deepfake and synthetic media detection. Leveraged CNN architectures with transfer learning and explored frequency-domain features (FFT artifacts) characteristic of GAN-generated images for improved discrimination. Designed data collection and labeling pipeline to curate balanced real vs. fake image datasets; applied augmentation and normalization for consistent ground truth quality. Evaluated model robustness across different GAN outputs (StyleGAN, DALL-E); documented detection accuracy, false positive rates, and escalated edge cases for review.
E-Commerce Sales & Customer Analysis | End-to-End Data Pipeline Project
January 1, 2024 – December 31, 2024
Collected and ingested raw CSV datasets into MySQL, performing systematic data cleaning and validation to establish ground truth for downstream analysis. Executed structured SQL queries (joins, CTEs, aggregations) to extract revenue trends, customer segmentation, and CLV metrics following defined analytical guidelines. Built Power BI dashboard with KPIs; documented all results, anomalies, and data quality failures with accurate status reports for stakeholder review. Delivered business recommendations by capturing and communicating insights across repeat vs. new customers, top products, and regional performance.
Customer Churn Analysis | Telecom Dataset - Data Capture & Insight Project
January 1, 2024 – December 31, 2024
Processed raw telecom dataset through structured data cleaning and feature engineering pipeline, adhering to defined procedures for each transformation step. Executed EDA capturing churn %, tenure vs. churn correlation, and payment type impact - accurately reporting each metric without compromising quality. Built dashboard storytelling around key findings; documented data anomalies and resolved within defined SLA timelines.
Sales Forecasting with Time Series | ARIMA & XGBoost Predictive Modeling
January 1, 2024 – December 31, 2024
Executed end-to-end data collection, cleaning, and feature engineering pipeline to capture monthly demand trends and seasonality patterns from sales records. Ran model training and evaluation cycles using ARIMA and XGBoost; compared model performance and accurately reported forecasting results and metrics. Generated inventory optimization insights by synthesizing model outputs; documented data quality issues impacting prediction accuracy throughout the execution lifecycle.
Microsoft Certified: Data Analyst Associate
Unknown
June 1, 2026 – Present
Power BI Foundation
Unknown
June 1, 2026 – Present
Data Analytics
Skillset Master
June 1, 2026 – Present
Python Programming Beyond – The Basics & Intermediate Training
Unknown
June 1, 2026 – Present
HTML, CSS & Bootstrap
Unknown
June 1, 2026 – Present
KultureHire – Data Analytics
Unknown
June 1, 2026 – Present
Data Analyst Beginner Course
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects show a strong interest and foundational skill set in AI/ML, data analysis, and computer vision, which aligns well with an AI/ML Engineer role. The diversity of projects, from sales forecasting to medical imaging and fake image detection, indicates a broad curiosity and willingness to apply skills across different domains. The virtual internships, while brief, suggest an initiative to gain practical exposure. The candidate is currently pursuing a Master's degree, indicating a commitment to continuous learning and professional development.
Soft Skills & Operational Fit
The candidate demonstrates attention to detail, process adherence, and the ability to execute repetitive tasks, as evidenced by project descriptions and virtual internships. They also highlight status reporting and communication skills, which are crucial for operational fit. However, the virtual internships are short-term and may not fully reflect real-world team collaboration or stress handling under sustained pressure.