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AI Engineer with less than a year in Python, Machine Learning & Deep Learning
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Assessing your cultural and operational fit
Computer Science student specializing in Artificial Intelligence with hands-on experience in Python, Machine Learning, Deep Learning, SQL, AI chatbots, and computer vision projects. Currently working as an AI Engineer Intern, with a strong interest in building intelligent, secure, and production-ready AI systems.
MIT School of Computing
B.Tech. · Computer Science Engineering (Artificial Intelligence & Analytics)
August 1, 2022 – June 30, 2026
B V Bhavans Llyods Vidya Niketan, Wardha
12th · CBSE
N/A – May 31, 2022
B V Bhavans Llyods Vidya Niketan, Wardha
10th · CBSE
N/A – May 31, 2020
Treewalker Technologies
AI Engineer Intern
March 1, 2026 – Present
India
AI Database Reading Agent
June 1, 2026 – Present
Building an AI-powered database/API reading agent that converts natural language questions into validated SQL for sales and inventory insights with tenant-based filtering. Designed a LangGraph workflow for query routing, schema context retrieval, SQL generation, validation, repair, execution, and business-friendly answer formatting. Integrated FastAPI, MySQL, Redis, Qdrant, Gemini embeddings, and SQLAlchemy with SQL guardrails to block unsafe commands and support secure read-only analytics.
Customer Churn Analysis
November 1, 2025 – November 1, 2025
Engineered predictive ML models achieving 85% accuracy in identifying high-risk customers across 7,000+ records with 21 features. Performed comprehensive EDA, data visualization, feature engineering, and hyperparameter tuning to optimize model performance.
Skin Cancer Detection – CNN
February 1, 2025 – May 1, 2025
Designed and trained a CNN model on the HAM10000 dataset (10,000+ images) to classify 7 types of skin lesions with high accuracy. Applied data augmentation and preprocessing for balanced class representation across all skin cancer categories. Deployed the model in a Flask web application enabling real-time skin mole image upload and instant cancer-type prediction.
Cultural Fit Analysis
The candidate's projects demonstrate a proactive and self-driven approach to learning and applying AI concepts. The diversity of projects (medical imaging, customer churn, natural language to SQL) indicates a broad interest in AI applications. The current internship aligns well with the target role of an AI Engineer, suggesting a strong commitment to the field. However, the candidate is still a student with limited professional experience, which might impact immediate cultural integration into a senior role without further mentorship.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex problems independently and deploy solutions. The internship experience suggests an ability to contribute to team projects and follow established workflows. However, without direct assessment data on soft skills, a comprehensive evaluation of operational fit is limited.