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AI Engineer with less than a year in Machine Learning & LLM Systems
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AI focused Computer Science postgraduate with strong hands on experience in machine learning, deep learning, and LLM based systems. Skilled in building end-to-end AI pipelines, including data preprocessing, model training, and explainability. Experienced in Python-based development for scalable AI solutions, with a focus on real-world applications such as medical imaging and on-device AI systems.
CUSAT
MSc · Computer Science with Artificial Intelligence
August 1, 2024 – June 30, 2026
Calicut University
BSc · Computer Science
August 1, 2021 – June 30, 2024
Mellon AI Labs
AI Full-Stack Engineering Intern
September 1, 2025 – Present
India
Elevate Labs
AI/ML Intern
January 1, 2025 – August 31, 2025
India
On-Device RAG Application with Gemma LLM and Orama Vector Database
October 1, 2024 – August 31, 2025
Developed an on-device AI system using Python, implementing Retrieval-Augmented Generation (RAG) to retrieve relevant information and generate accurate responses. Used Gemma LLM for local inference, enabling offline, prompt-based response generation. Implemented semantic search using Orama vector database and embeddings, converting text into numerical vectors for efficient similarity-based retrieval. Built a preprocessing pipeline (Python) including text cleaning, chunking, embedding generation, and indexing for better search performance. Combined retrieval + LLM generation, improving response quality by providing relevant context to the model. Designed the system for local deployment, ensuring data privacy, reduced latency, and real-time performance. Technologies: Python, Gemma LLM, RAG, Orama Vector DB, Embeddings, NLP
Autism Detection from 3D Brain MRI using Deep Learning
August 1, 2023 – March 31, 2024
Developed a deep learning pipeline using Python to classify 3D MRI brain scans for autism detection. Performed MRI preprocessing (skull stripping, normalization) using medical imaging libraries to improve data quality. Implemented CNN models using TensorFlow/Keras for feature extraction and classification of brain images. Applied SHAP for model interpretability, identifying important brain regions influencing predictions. Evaluated model performance using standard metrics to ensure reliability. Technologies: Python, TensorFlow, Nilearn, ANTs, DIPY, SHAP
Intelligent Resume Screening System using Machine Learning
January 1, 2023 – July 31, 2023
Developed a resume screening system using Python, automating candidate selection by matching resumes with job descriptions. Applied NLP techniques (tokenization, TF-IDF) to convert resume text into numerical features for analysis. Built a machine learning-based classification system to categorize resumes into relevant job roles based on content. Implemented cosine similarity scoring, comparing resume and job description vectors to rank candidates by relevance. Performed data preprocessing and feature extraction using Pandas and Scikit-learn to improve model performance. Technologies: Python, Scikit-learn, Pandas, NLP, TF-IDF, Cosine Similarity, Streamlit
LabourJet - Smart Labour Availability Web Application
June 1, 2022 – December 31, 2022
Developed a web-based service booking platform using Python, connecting users with available workers through a structured system. Designed backend logic and workflows in Python, handling user requests, job assignments, and service management. Implemented database management using MySQL, storing user data, worker details, and booking information efficiently. Built separate modules for user, admin, and worker roles, enabling smooth interaction and system control. Completed the full development lifecycle, from system design and database schema creation to implementation and testing. Technologies: Python, MySQL, Backend Development
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in diverse AI applications, including medical imaging, on-device RAG, and intelligent screening systems. This breadth of interest and application aligns well with an innovative and problem-solving culture often found in AI engineering teams. The internships at 'Mellon AI Labs' and 'Elevate Labs' further indicate a proactive approach to gaining industry experience. The candidate's educational background, including a Master's in Computer Science with AI, shows a dedicated pursuit of knowledge in the field. The projects also highlight an ability to work on full-stack aspects (LabourJet) and focus on practical, deployable solutions (On-Device RAG).
Soft Skills & Operational Fit
The candidate's project descriptions and internship experiences suggest an ability to collaborate in teams (Git, agile workflows) and manage full development lifecycles. The focus on real-world applications and problem-solving indicates a practical and results-oriented approach. However, without specific psychometric or communication test results, a deeper assessment of soft skills like logical reasoning, work attitude, stress handling, and team collaboration is not possible.