Amna Siddiqui

PROFESSIONAL PROFILE Machine Learning and AI Engineer specializing in secure, scalable, and production-ready intelligent systems with strong DevOps and MLOps integration. Experienced in building end-to-end ML pipelines including data ingestion, model training, evaluation, containerized deployment, and CI/CD automation. Strong expertise in Python, LLM-based systems, computer vision, and AI-driven security solutions optimized for performance, scalability, and reliability. EDUCATION Electrical Engineering SEECS , Islamabad , 2.21 (2026) INTERNSHIP EXPERIENCE ForthLogic AI - Machine Learning Engineer 12-Jul-2025 - 22-Jan-2026 Built and deployed a Dockerized AI-powered university search engine chatbot using FastAPI for scalable semantic search and real- time inference. UniBot: Implemented LLM-based RAG pipelines using embeddings and vector search to deliver accurate, context- aware responses. BuzzBreach: Developed an end-to-end computer vision pipeline for satellite image analysis using CNNs and integrated results into an AI chatbot system. CHATLEY: Deployed production-grade ML APIs using Docker, REST services, and CI/CD pipelines for automated and reliable model serving. VAPI: Engineered Chatley, an LLM chatbot with fine-tuning, embedding generation, and real-time inference capabilities. Fuddi: a computer vision application using CNN-based image classification with full preprocessing and deployment workflows. n8n: Automated end-to-end ML workflows using n8n for data pipelines, model execution, and API orchestration. Centre for Advanced Research in Engineering (CARE) - AI/ML Intern 15-Jun-2024 - 15-Sep-2024 Implemented ML-based anomaly detection systems for fraud and spoofing in secure communication pipelines. Developed RSA- encrypted APIs with ML-assisted intrusion monitoring mechanisms. Built feature engineering pipelines and optimized classifiers for security datasets. Worked on secure model deployment practices and encrypted ML inference workflows. ONT-SDN Lab (SEECS) Research Intern 05-Jun-2025 - 05-Sep-2025 • Applied ML models for real-time traffic anomaly detection in SDN simulations. • Built streaming ML pipelines using Redis Pub/Sub for real-time data ingestion and prediction. • Integrated blockchain-secured ML logging using Hyperledger Fabric for immutable network event validation. • Optimized network-security ML systems for scalability and low-latency deployment. FINAL YEAR PROJECT Federated Learning IoT Security (FYP) Designed federated ML models for IoT anomaly detection with local training, secure aggregation, and API integration. TECHNICAL EXPERTISE TECHNICAL SKILLS ML/AI: n8n, Scikit-learn, TensorFlow, PyTorch, XGBoost, Autoencoders, Computer Vision (OpenCV, CNNs) MLOps & Deployment:

Machine Learning and AI Engineer specializing in secure, scalable, and production-ready intelligent systems with strong DevOps and MLOps integration. Experienced in building end-to-end ML pipelines including data ingestion, model training, evaluation, containerized deployment, and CI/CD automation. Strong expertise in Python, LLM-based systems, computer vision, and AI-driven security solutions optimized for performance, scalability, and reliability.