AI/ML Engineer | GenAI | RAG | NLP
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Assessing your cultural and operational fit
StudyHelper
April 7, 2026 – Present
GenAI-powered learning assistant that uses RAG, Groq, and YouTube transcripts to generate summaries, Q&A, and quizzes from lectures.
View ProjectNeuroBarrier-AI
April 7, 2026 – Present
ML-based application for predicting BBB permeability and aqueous solubility from molecular features (ADMET-focused).
View Projectclassical-ml-projects
November 22, 2025 – Present
A collection of classical Machine Learning projects built using NumPy, Pandas, Matplotlib, and Scikit-Learn. Covers regression, classification, clustering, dimensionality reduction, and full ML workflows. A complete hands-on portfolio demonstrating core ML skills before Deep Learning.
View Projectclassical-machine-learning
November 18, 2025 – November 22, 2025
My personal classical machine learning learning journey — 20+ models trained, evaluated, and documented using scikit-learn and Google Colab.
View ProjectEcoMission
July 25, 2025 – July 25, 2025
EcoMission 🌿 EcoMission is a simple yet impactful web application that empowers users to report local environmental issues like pollution, waste dumps, or dirty lakes. Users can upload images/videos, describe the issue, and suggest a date for community action. Others can view issues and join events to collectively resolve them.
View ProjectSmartAssistantForReaseachPaper
July 13, 2025 – July 14, 2025
SmartAssistantForReaseachPaper — GitHub repository
View ProjectCultural Fit Analysis
The candidate's project portfolio shows a strong inclination towards personal learning and exploration in Machine Learning and AI. The diversity of projects, from classical ML to GenAI and even web development, suggests a curious and self-driven individual. However, the lack of professional experience or team-based projects makes it difficult to assess collaboration or alignment with a structured team environment. The target role is 'Data Scientist', and while the projects align with ML aspects, there's less evidence of data engineering, statistical modeling, or A/B testing, which are common in data science roles.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.