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AI Engineer with 1+ years in Deep Learning & Data Pipelining
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Computer Engineering graduate (2026) with a strong foundation in SQL, RDBMS, Python, Data Structures, and Algorithms. Experienced in Requirement Analysis, Data Pipeline Development, and building AI-powered Technology Solutions, including an IEEE-published research project that processed 1,500+ image records through a complete data preprocessing, transformation, and deep learning pipeline. Familiar with Data Warehousing, Data Quality, and ETL concepts, with a collaborative mindset and strong Analytical Problem-Solving skills to deliver scalable, data-driven business solutions.
Vidyavardhini College of Engineering and Technology
B.E · Computer Engineering
November 1, 2022 – May 1, 2026
Braille to Text and Speech Conversion
August 1, 2025 – May 1, 2026
Designed and developed an AI-powered Braille-to-Text and Speech conversion system, processing 1,500+ Braille images to improve accessibility for visually impaired users. Trained and optimized deep learning models (VGG16, LeNet-5, MobileNetV2), achieving 91% test accuracy across a dataset of 1,500+ Braille images. Applied data preprocessing, augmentation, pruning, and quantization techniques, reducing model size by 75% (5.6 MB to 1.4 MB) for efficient mobile deployment. Integrated TensorFlow Lite, Text-to-Speech (TTS), and voice command functionality into an Android application, enabling fully offline real-time inference and speech conversion. Achieved 96.4% character recognition accuracy and a 0.97 macro AUC score across 300 test images, demonstrating strong model generalization and classification reliability. Research paper accepted for publication - IEEE Conference. Team of 5 members.
Spam Image Detection using Deep Learning
August 1, 2025 – November 1, 2025
Collected and curated a dataset of 2,000+ spam and legitimate images using web crawling and automated data acquisition techniques for model training and evaluation. Designed and implemented a complete data preprocessing, transformation, augmentation, and validation pipeline, ensuring high-quality and standardized image data. Trained and optimized a MobileNetV2 transfer learning model, achieving 98.07% classification accuracy and 0.987 AUC on unseen test datasets.
Introduction to Cloud Infrastructure: Describe cloud concepts
Azure
June 18, 2026 – Present
Introduction to Cyber-Security
CISCO
June 18, 2026 – Present
Manage Identities in Microsoft Entra ID
Microsoft
June 18, 2026 – Present
Data Structures and Algorithms in Java
Infosys Springboard
June 18, 2026 – Present
TATA Cybersecurity Analyst Job Simulation
Forage
June 18, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects show a strong focus on AI/ML applications, particularly in image processing and accessibility, which aligns well with innovative and impact-driven environments. The involvement in a team project (Braille to Text and Speech Conversion) indicates a collaborative mindset. The breadth of technical skills, including various programming languages, database concepts, and cloud platforms, suggests adaptability. However, the projects are all academic, and there is no professional experience, which limits the assessment of cultural fit in a corporate setting. The target role of 'Data Science Engineer' aligns well with the candidate's project experience and technical skills.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical problem-solving skills and a collaborative mindset through their project work. The extracurricular activity as a Treasurer suggests organizational and responsibility skills. However, the psychometric test score is 0, which is insufficient to assess logical reasoning, work attitude, stress handling, and team collaboration effectively. More data is needed for a comprehensive evaluation of soft skills and operational fit.