AI Engineer with less than a year in Machine Learning & Computer Vision
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Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Aspiring Machine Learning Engineer with hands-on experience in multimodal AI, computer vision, OCR systems, and transformer-based models. Skilled in developing and deploying AI applications using Python, Hugging Face, and Vision-Language Models (Qwen-VL). Strong foundation in machine learning, deep learning, document AI, and data processing.
D.Y. Patil College of Engineering and Technology, Kolhapur
B.Tech · Computer Science and Engineering
November 1, 2022 – June 1, 2026
IIT Bombay
EdTech Intern
July 1, 2025 – October 1, 2025
India
AICTE
TechSaksham - AI Intern
November 1, 2024 – December 1, 2024
India
OCR Management System
January 1, 2026 – January 1, 2026
Developed a multimodal Document AI system using Qwen-VL for extracting structured information from documents and images. Leveraged transformer-based models for OCR and document understanding. Deployed on Hugging Face Spaces.
View ProjectTemporal Emotion Transition Modeling
October 1, 2025 – October 1, 2025
Conducted exploratory analysis on multimodal time-series data (EEG, GSR, eye-tracking, facial expressions). Identified emotion transition patterns and engagement trends.
Image Classification Project
December 1, 2024 – December 1, 2024
Built an image classification model using foundational CNN architectures. Performed data preprocessing, training, and evaluation on image datasets.
Deloitte Australia - Data Analytics Job Simulation
Deloitte Australia
June 1, 2026 – Present
Generative AI Exchange Program - Google Cloud Skills Boost (1st Rank)
Google Cloud Skills Boost
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects and internships demonstrate a strong interest and foundational experience in AI/ML, particularly in multimodal AI and computer vision. The diversity of projects (OCR, temporal emotion modeling, image classification) and certifications (Google Cloud Generative AI) indicates a broad curiosity and commitment to the field. This aligns well with a dynamic, innovation-focused AI engineering environment. The academic nature of all projects and limited professional experience suggest a learning-oriented individual who would likely integrate well into a team that values continuous development.
Soft Skills & Operational Fit
The candidate's project descriptions and internship experiences suggest an ability to work on complex problems, analyze data, and develop solutions. The recognition for outstanding performance during an internship and securing 1st rank in a Generative AI program indicates a proactive and high-achieving work attitude. However, without specific psychometric test results, a detailed assessment of stress handling, logical reasoning, and team collaboration is not possible.