IT graduate with strong foundations in software development and a hands-on focus on AI/ML - spanning deep learning, RAG pipelines, and REST API engineering. Completed internships at Shell Edunet (AICTE) and IBM SkillsBuild; selected for Amazon ML Summer School 2025. Open to SDE and AI/ML roles where I can build reliable, scalable software.
Overview
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Certification
Work History
AI/ML Intern
Shell Edunet Foundation
08.2025 - 10.2025
Built end-to-end ML pipelines with scikit-learn and TensorFlow; applied cross-validation techniques that improved model generalisation and reduced overfitting on tabular datasets.
Designed automated data quality checks and preprocessing pipelines, improving reproducibility and cutting training overhead across multiple experiments.
Drove model evaluation benchmarking across accuracy, precision, recall, and F1-score; documented results for cross-functional stakeholder review.
AICTE - Skill4Future
AI/ML Intern
IBM SkillsBuild
08.2025 - 09.2025
Delivered hands-on AI/ML projects on IBM Cloud covering NLP, predictive analytics, and data pipelines using Watson AI services and Watson Studio.
Deployed AI applications on cloud infrastructure; gained enterprise exposure to model lifecycle management and scalable ML workflows.
Claude Code 101 & AI Fluency, Anthropic, 2026-05-01
RHCSA OpenStack, Red Hat, 2025-04-01
Achievements Leadership
Amazon ML Summer School 2025 - Competitively selected for national Girl Leading Tech Cohort; advanced training in Deep Learning, Model Evaluation, and AWS SageMaker with Amazon engineers.
Mentorship & Community: SheCanCode Mentor (2025-07-01); GirlScript Summer of Code 2025 - Campus Ambassador & Mentor; Google Women Techmakers Member (2024-01-01-Present); SheFi Scholar Season 14 - global Web3/DeFi cohort.
Projects
DDoS Detection: Autoencoder + Random Forest Hybrid, Python, TensorFlow, Scikit-learn, Pandas, 2026-04-01, Designed hybrid deep learning system combining autoencoders (60% dimensionality reduction) with Random Forest on CIC-DDoS2019 dataset, achieving 98.4% standalone and 99% hybrid accuracy., Built full training-to-evaluation pipeline with reproducible benchmarking and detailed performance reporting.
PDF Q&A System (RAG + LLMs), Python, LangChain, FAISS, OpenAI API, Sentence Transformers, Streamlit, 2026-02-01, Architected production RAG pipeline with FAISS vector store and LLM integration for natural language queries over PDFs; achieved sub-second retrieval with hallucination-reduced, cited answers.
AI Phishing & Malicious URL Detection, Python, XGBoost, Scikit-learn, Flask, REST API, 2025-11-01, Engineered 30+ URL features; trained XGBoost classifier at 96%+ accuracy, deployed as Flask REST API with sub-100ms inference latency for real-time threat classification at scale.
Smart Health Monitoring API, Python, FastAPI, MQTT, IoT Sensors, JWT, Docker, 2025-01-01, Architected MQTT-based IoT REST API with JWT authentication and threshold-driven clinical alerting for real-time patient vitals; containerised with Docker for reproducible, portable deployment.