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AI Engineer with less than a year in Machine Learning, Deep Learning, and Agentic AI, seeking to bui
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Assessing your cultural and operational fit
Aspiring AI/ML Engineer with hands-on experience in machine learning, Agentic AI, and RAG systems. Skilled in building end-to-end AI solutions using Python, SQL, and deploying applications with Streamlit.
Savitribai Phule Pune University
B.E. · Information Technology
N/A – June 30, 2027
Rubixe
Artificial Intelligence Machine Learning Intern
October 1, 2025 – March 31, 2026
India
YBI-Foundation
Big Data and Cloud Computing Intern
February 1, 2025 – March 31, 2025
India
Fruit Classification and Quality Detection using Deep Learning
June 1, 2026 – Present
Designed and developed a Convolutional Neural Network (CNN)-based image classification system to identify fruit types and detect freshness, trained on 1000+ labeled images across multiple fruit categories. Applied data preprocessing and augmentation techniques, including resizing, normalization, and transformations, increasing dataset diversity by 2x and improving model generalization. Achieved 92% classification accuracy on the validation dataset using TensorFlow and Keras. Enhanced model performance through hyperparameter tuning, reducing validation loss and improving prediction consistency. Built and deployed a Flask-based web application enabling real-time predictions with response time under 2 seconds for user-uploaded images.
Advanced Agentic AI System using RAG and Multi-Agent Architecture
June 1, 2026 – Present
Designed and developed an advanced multi-agent Agentic AI system using LangGraph, improving workflow efficiency by ~25-30%. Implemented Retrieval-Augmented Generation (RAG) with ChromaDB and integrated real-time web search, increasing information retrieval accuracy by ~25%. Built intelligent routing mechanisms to dynamically switch between retrieval, query reformulation, and external search. Developed self-correcting pipelines with iterative query refinement and fallback strategies, improving response relevance. Incorporated fact-checking and safety validation layers to reduce incorrect or unsafe responses. Deployed an interactive Streamlit-based interface supporting real-time AI interaction, improving user response efficiency by ~20%.
Generative AI
Google Cloud
June 1, 2026 – Present
AI Partitioner
Coursera
June 1, 2026 – Present
Data analytics
Deloitte
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest and practical application in cutting-edge AI fields like Agentic AI and RAG, which aligns well with an innovative and forward-thinking culture. The diversity of projects (image classification, multi-agent systems) and certifications (Generative AI, AI Partitioner) indicates a proactive learning attitude. The internship experience, while foundational, shows an early engagement with industry practices. The target role of 'AI Systems' is well-supported by the candidate's project portfolio and stated skills.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on end-to-end solutions, from model development to deployment. The focus on improving efficiency and accuracy in projects suggests a results-oriented approach. However, without direct interview data, it's difficult to assess collaboration, stress handling, or specific work attitude beyond what's implied by project completion.