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AI Engineer with 2+ years in Machine Learning, Generative AI, and Agentic Systems.
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Computer Science undergraduate (6th Semester, COMSATS University Attock) specializing in Machine Learning, Deep Learning, NLP, Generative AI, and Agentic AI. Experienced in building production-level AI systems including RAG-based chatbots, intelligent agents, text summarization, and machine translation. Strong focus on real-world AI applications using LangChain, LangGraph, and modern ML/DL frameworks.
COMSATS University Attock
BS Computer Science · Computer Science
N/A – June 30, 2026
RAG-based Intelligent Chatbot
June 24, 2026 – Present
Purpose: Built a domain-specific chatbot to deliver accurate contextual responses using RAG. Achieved 95% response accuracy and reduced hallucinations by 70% using vector search optimization. Integrated multiple tools including web search, weather APIs, and stock APIs for real-time responses. Implemented vector databases (FAISS, ChromaDB) for semantic search and context retrieval. Designed multi-step reasoning pipeline using LangGraph for intelligent decision-making.
AI Blog Generation Agent
June 24, 2026 – Present
Purpose: Automated blog writing agent that plans, researches, and generates complete blogs automatically. Reduced manual content creation time by 75% through full automation pipeline. Improved content generation efficiency by 80% through automated pipelines. Implements dynamic research pipeline to fetch and validate external data before writing. Integrated image generation and contextual placement within blog content using gemini api.
WhatsApp Business Automation Chatbot
June 24, 2026 – Present
Purpose: Automated business customer support using AI chatbot. Used RAG to ensure responses are strictly business-specific with 85% accuracy. Reduced manual customer handling effort by 60% and improved response consistency. Designed scalable system for handling multiple business use cases.
Text Summarization & Machine Translation
June 24, 2026 – Present
Purpose: Built NLP models for summarization and English – Urdu translation. Achieved improved sequence accuracy and reduced loss during training using attention mechanism. Achieved consistent sequence generation performance on custom dataset.
Machine Learning & Deep Learning Systems
June 24, 2026 – Present
Purpose: Developed multiple ML/DL models for prediction and classification tasks. Achieved strong predictive performance across regression and classification problems. Built prediction models including house price, insurance premium, and laptop price prediction. Implemented CNN-based projects including skin cancer and brain tumor detection. Built full-stack ML apps with FastAPI for real-world deployment.
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest and practical application across various AI domains, including RAG, generative AI, NLP, and traditional ML/DL. This diversity, coupled with a clear focus on building intelligent systems, suggests a good cultural fit for an innovative AI-centric team. The candidate's stated interests in 'Agentic Systems' and 'System Design' further align with a forward-thinking AI engineering culture. However, all projects are academic, which might indicate a lack of experience in a professional, collaborative team environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a problem-solving mindset and an ability to translate theoretical AI concepts into practical applications. The focus on automation and efficiency (e.g., reducing manual content creation time, improving response consistency) suggests a results-oriented approach. However, without specific psychometric or behavioral test results, it's difficult to assess stress handling, team collaboration, or other soft skills directly.