Faara Ramsy
AI EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer with less than a year in Machine Learning, Deep Learning, and Generative AI
Highly motivated second-year Artificial Intelligence and Data Science undergraduate with hands-on experience in end-to-end machine learning pipelines and Generative AI (RAG) workflows, exploratory data analysis, and predictive modelling. Proficient in Python, Pandas, NumPy, scikit-learn, Power BI with a strong foundation in statistical thinking and data-driven problem solving. Finalist in the ModelX AI Hackathon. Passionate about solving real-world problems using data-driven approaches and contributing to impactful Projects.
Informatics Institute of Technology
Bcs (Hons) · Artificial Intelligence and Data Science
August 1, 2025 – June 30, 2026
Telford International School – Galle
International Advanced Level
June 1, 2018 – May 31, 2024
Telford International School – Galle
IGSCE Ordinary Level
June 1, 2018 – May 31, 2022
Telecom Customer Churn Dashboard & Predictive Pipeline
June 23, 2026 – Present
Wrote and optimized complex SQL queries to extract, clean, and structure raw transactional and customer data from relational databases, ensuring data integrity for downstream analysis. Designed an interactive Power BI dashboard to visualize key metrics, identifying a 26.58% overall churn rate across 7K customers, with critical risk concentrations in month-to-month contracts and early-tenure profiles (<6 months). Implemented a comparative ML experiment (Decision Tree and Neural Network) using scikit-learn and TensorFlow to predict high-risk churn candidates, balancing precision and recall optimizing retention strategies. Tools – Python, SQL, PowerBI, Pandas, NumPy, Scikit-learn, TensorFlow (Keras), Matplotlib, Seaborn.
View ProjectRAG-Powered Document Q&A system
June 23, 2026 – Present
Built an end-to-end Retrieval Augmented Generation (RAG) pipeline enabling natural language querying over any PDF document. Implemented semantic search using HuggingFace sentence embeddings and FAISS vector store for context retrieval. Integrated Llama 3.1 LLM via Groq API with constrained prompt engineering to eliminate hallucination. Deployed interactive web interface using Gradio with real-time source attribution showing which document chunks informed each answer. Tools – Python, LangChain, FAISS, HuggingFace, Groq API, Gradio, Google Colab, Agentic AI workflows.
View ProjectRelational Database Project: Café Management System
June 23, 2026 – Present
Developed a fully normalized relational database using MySQL to manage online orders, inventory, staff, and delivery operations. Designed schema for online ordering, staff management, and delivery tracking with 10+ tables. Wrote complex SQL queries for business intelligence and data manipulation. Tools – MySQL, phpMyAdmin. SQL, Database Design, EER Modeling.
DSGP: Multi-Label Tomato Leaf Disease Detection and Severity Estimation
June 23, 2026 – Present
Conducted literature review to identify a critical research gap existing models classify single diseases only, failing to address real-world multi-disease scenarios on a single leaf. Designed and built an independent severity estimation pipeline using CNNs (Resnet50, MobileNetV2, EfficientNetB0) to quantify disease severity from tomato leaf images. Applied HSV colour space segmentation to isolate and calculate the percentage of infected leaf area, enabling precise severity scoring. Deployed model via Flask REST API for real-time inference. Tools – Python, TensorFlow (Keras), CNNs, OpenCV, HSV Segmentation, NumPy, Matplotlib, Flask API.
View ProjectDementia Risk Prediction Model using Non-Medical Factors (AI Hackathon Project Finalist)
June 23, 2026 – Present
Built an end-to-end Machine learning Model to predict dementia risk using non-medical demographic, lifestyle, and functional data. Achieved a strong predictive performance, with ROC AUC (of 0.97) used as the primary evaluation metric, and visualized results using ROC curves and confusion matrices. Tools – Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn.
View ProjectFinalist in ModelX AI Hackathon
Unknown
June 1, 2026 – Present
Introduction to Prompt Engineering for Generative AI
Unknown
June 1, 2026 – Present
Generative AI: Introduction to Large Language Models
Unknown
January 1, 2026 – Present
Introduction to AI concepts
Microsoft Learn
January 1, 2025 – Present
Awarded with Extraordinary Performance for IAL Results
Unknown
January 1, 2024 – Present
Awarded with Best Results for IGCSE O-level Result
Unknown
January 1, 2022 – Present
Cultural Fit Analysis
The candidate's academic projects showcase a diverse range of applications for AI, from business intelligence (churn prediction) to healthcare (disease detection, dementia risk) and natural language processing (RAG system). This breadth of interest and application aligns well with an innovative and problem-solving culture. Their involvement in various student societies and leadership roles (Deputy Head Girl, Vice President of Islamic Society) indicates a proactive, collaborative, and community-oriented mindset, which are positive indicators for cultural fit. However, all experience is academic, so adaptability to a professional, fast-paced industry environment is an unknown.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving, analytical thinking, and teamwork skills through their project descriptions and extra-curricular activities. Their involvement in student governance and societies indicates leadership potential and adaptability. However, the lack of professional work experience means their operational fit in a corporate environment, including handling complex project management, stakeholder communication, and navigating organizational structures, is yet to be proven. Their academic background and project work suggest a good capacity for independent work and learning.