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AI Engineer with 2+ years in Data Analytics & Machine Learning
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Results-driven AI/ML Developer with hands-on experience building end-to-end machine learning pipelines, deep learning models, and LLM-powered applications. Proficient in Python, TensorFlow, PyTorch, and Hugging Face Transformers with demonstrated expertise in computer vision, NLP, RAG systems, and data analytics. Passionate about deploying AI solutions that solve real-world problems.
MES College, Marampally, Ernakulam
M.Sc. Electronics · Electronics
August 1, 2020 – June 30, 2022
Morning Star Home Science College, Angamaly
B.Sc. Physics with Applied Electronics · Physics with Applied Electronics
N/A – June 30, 2020
Bsoft Network Solutions
STEM Trainer
October 1, 2022 – April 1, 2025
Cochin, Kerala, India
MedInsight — Medical RAG System
January 1, 2025 – January 1, 2025
Built a Retrieval-Augmented Generation (RAG) medical assistant enabling semantic search, AI-powered summarization, and question answering over clinical documents and medical reports. Implemented OCR-based text extraction for scanned PDFs and medical images using Tesseract OCR, supporting both digital and physical document ingestion. Designed a FAISS vector retrieval pipeline with Hugging Face embeddings for efficient semantic indexing and context-aware response generation. Integrated local LLM inference using Ollama and Phi-3 Mini to provide fully offline medical chat and report analysis via an interactive Streamlit UI.
Indian Currency Note Recognition System for Visually Impaired Users
January 1, 2025 – January 1, 2025
Developed a real-time Indian currency detection system using YOLOv8 to recognize six denominations (₹10–₹500) from live webcam feeds. Integrated text-to-speech (TTS) output using pyttsx3 to announce detected currency notes audibly, making the system accessible for visually impaired users. Implemented confidence-based detection, speech debouncing, and real-time bounding box visualization using OpenCV. Trained a YOLOv8 model on a Roboflow-hosted dataset using Google Colab and built a Python-based real-time inference application in VS Code.
Smart Waste Classification System
January 1, 2025 – January 1, 2025
Developed a deep learning waste classification system identifying 10 waste categories (recyclable vs. non-recyclable) from image inputs using CNN-based architectures. Applied transfer learning in both TensorFlow and PyTorch to achieve multi-class image classification with high accuracy on real-world waste datasets. Built a Streamlit dashboard for image upload, confidence visualization, probability analysis, and side-by-side framework comparison (TensorFlow vs. PyTorch).
Sleep Disorder Prediction using Machine Learning
January 1, 2025 – January 1, 2025
Trained classification models (Logistic Regression, KNN, SVM, Random Forest) to predict sleep disorders — Insomnia, Sleep Apnea, and None —from healthcare lifestyle data. Performed end-to-end data preprocessing, EDA, and feature engineering to improve data quality and boost model accuracy. Applied hyperparameter tuning and k-fold cross-validation to optimise model performance; serialised final model with Joblib for deployment-ready inference.
Pharmaceutical Sales — EDA & Analytics
January 1, 2025 – January 1, 2025
Conducted comprehensive EDA on pharmaceutical sales data to surface revenue drivers, regional pricing patterns, and customer behaviour insights. Delivered analytical visualisations and reports evaluating product performance, sales distribution, and marketing effectiveness to support data-driven decision-making.
Retail Sales Analytics — SQL & Python
January 1, 2025 – January 1, 2025
Wrote advanced SQL queries using window functions, GROUP BY aggregations, and ranking functions to analyse customer behaviour, product performance, and revenue KPIs. Performed monthly trend analysis and correlation studies using pandas, identifying high-revenue regions and the impact of discounts on profitability.
Sales Analytics Dashboard — Power BI
January 1, 2025 – January 1, 2025
Designed an interactive Power BI dashboard with KPI cards (sales, profit, orders, margin), regional heat maps, customer segmentation visuals, and product performance trackers. Implemented DAX calculations, dynamic filters, and sales target gauges enabling real-time business monitoring and executive reporting.
Professional Certification in AI/ML with Data Science
SMEC Technologies, Kaloor, Kerala
June 1, 2026 – Present
IBM Python for Data Science, AI & Development
Coursera
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio demonstrates a strong interest in applying AI/ML to real-world problems, including healthcare (MedInsight, Sleep Disorder Prediction), accessibility (Indian Currency Note Recognition), and environmental sustainability (Smart Waste Classification). This diversity indicates a proactive and problem-solving mindset. The 'STEM Trainer' experience suggests an ability to explain complex topics, which could be beneficial for team collaboration and knowledge sharing. However, the experience is primarily in personal projects, and there is limited information on collaborative work within a professional AI/ML team setting.
Soft Skills & Operational Fit
The candidate's resume highlights 'Analytical Thinking', 'Technical Communication', 'Team Collaboration', 'Adaptability', and 'Time Management' as core competencies. While these are crucial for operational fit, the provided data does not offer specific examples or assessments to validate these claims beyond the project descriptions. The 'STEM Trainer' role suggests some communication and collaboration skills, but direct evidence for a senior AI Engineer role is limited.