AI Engineer with less than a year in LLM Integration & Voice AI
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Final-year BSc (Hons) Computer Science with Artificial Intelligence undergraduate with nearly one year of industry experience in software engineering and AI-powered applications. Experienced in LLM integration, RAG systems, voice AI pipelines, and modern software development practices. Seeking opportunities in AI/ML, Generative AI, or Software Engineering.
Coventry University (UK)
BSc(Hons) in Computer Science With Artificial Intelligence · Computer Science With Artificial Intelligence
September 1, 2023 – November 1, 2026
Cloud Solutions International
Internship
July 1, 2025 – Present
Saudi Arabia
LLM Evaluation Research
June 23, 2026 – Present
Conducted a systematic evaluation of 7 open-source LLMs across 50 curated questions spanning 10 domains. Benchmarked models on hallucination resistance, factual accuracy, and response latency using Promptfoo, with Gemini 2.5 Flash as an independent evaluator. Designed structured evaluation pipelines (Python, YAML) integrating Ollama and Groq APIs for automated testing. Key findings: Model size showed low correlation with hallucination rates; LLaMA 4 Scout achieved the highest performance with a 99% weighted score.
Serene - Mental Health Chatbot
June 23, 2026 – Present
Built a context-aware mental health assistant using RAG grounded in clinical literature. Designed a dual-LLM system (LLaMA-3.3 70B via Groq with Gemini 2.5 Flash fallback) and implemented real-time crisis detection (4 severity levels). Integrated PHQ-9 & GAD-7 assessments and sentiment-based mood tracking with a predictive analytics dashboard. Developed a full-stack solution (Flask, Next.js, Flutter) with SSE-based real-time streaming
Stock Price Prediction (Samsung)
June 23, 2026 – Present
Developed a stock price prediction model using regression with data from yfinance; implemented preprocessing, feature engineering, and visualization (Matplotlib).
Disease Prediction Using Symptoms
June 23, 2026 – Present
Created a disease prediction web app using a Random Forest model with Flask and JavaScript for real-time interaction.
Water Quality Monitoring System (Group)
June 23, 2026 – Present
Developed a real-time water quality monitoring system using ESP32 and sensors to track pH, turbidity, and temperature; focused on sensor calibration and microcontroller integration.
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in practical, impactful AI applications, such as mental health assistance and LLM evaluation. Their academic background in Computer Science with AI, combined with an internship focused on AI/LLM development, aligns well with a company seeking an AI Engineer. The diversity of projects, from LLM research to full-stack chatbot development and even embedded systems (water quality), indicates a broad technical curiosity and adaptability. The candidate's skills span multiple areas (AI/ML, Web, DevOps, Mobile), suggesting a versatile individual who can contribute across different facets of a project. The focus on real-time systems and optimization also points to a results-oriented mindset.
Soft Skills & Operational Fit
The candidate's project descriptions and professional experience highlight a proactive and structured approach to problem-solving, particularly in designing and optimizing real-time AI systems. The detailed descriptions of their work on LLM evaluation and the mental health chatbot suggest strong analytical and design thinking skills. Their internship experience indicates an ability to work within a professional environment, handling system migrations, feature development, and debugging. The academic project involving a group suggests some level of teamwork, though further details on collaboration style are not available.