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AI Engineer with less than a year in Machine Learning and Python.
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Assessing your cultural and operational fit
Highly motivated Computer Science student with a strong foundation in Python and Machine Learning. Experienced in developing RAG-based AI assistants, hybrid recommendation systems, and end-to-end ML pipelines for predictive analytics. Proficient in various ML frameworks, data preprocessing, and deployment, seeking to leverage skills in innovative AI/ML projects.
LJ Institute Of Engineering & Technology
COMPUTER SCIENCE · Computer Science
August 1, 2023 – June 30, 2027
RAG-Based AI Teaching Assistant with Semantic Video Retrieval
June 4, 2026 – Present
Developed a RAG-based AI assistant that transcribes lecture videos using Whisper, generates embeddings via BGE-M3 (Ollama), and performs cosine similarity search to retrieve relevant timestamped content. Integrated LLaMA 3.2 for context-grounded response generation, enabling precise mapping of user queries to specific lecture videos and timestamps.
View ProjectHybrid Movie Recommendation System
June 4, 2026 – Present
Built a hybrid recommendation system combining content-based and collaborative filtering to generate personalized movie suggestions. Designed a weighted scoring mechanism to balance multiple signals and improve recommendation relevance. Developed and deployed a FastAPI-based API and evaluated performance using Precision@K.
View ProjectHousing Price Predictor
June 4, 2026 – Present
Built an end-to-end ML pipeline to predict housing prices using preprocessing, feature transformation, and regression models. Implemented automated data preprocessing with imputation, scaling, and categorical encoding using Scikit-Learn pipelines. Evaluated and optimized model performance using cross-validation and deployed a reusable prediction system by persisting the trained model and pipeline.
View ProjectCultural Fit Analysis
The candidate's academic projects demonstrate initiative and a focus on practical application of AI/ML concepts. The diversity of projects (RAG, recommendation systems, predictive modeling) suggests a broad interest in AI domains. However, without professional experience or team-based project details, assessing cultural fit beyond technical alignment is challenging.
Soft Skills & Operational Fit
The provided data is insufficient to assess soft skills or operational fit. The candidate's profile is purely academic with no work experience.