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AI Engineer with less than a year in Machine Learning, Deep Learning, and Generative AI
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Electrical Engineering graduate from UET Peshawar with hands-on experience in AI, Machine Learning, Deep Learning, and Generative AI. Skilled in building AI-powered applications using Python, TensorFlow, Scikit-learn, LangChain, FastAPI, and Streamlit, including LLM-based chatbots and end-to-end ML solutions. Passionate about LLMs, AI Agents, and intelligent automation seeking AI/ML Internship, AI Associate, or Junior AI Engineer roles.
Corvit Institute Mardan
Huawei HCCDA-AI Bootcamp · Python Programming, Data Structures, Artificial Intelligence Fundamentals, Machine Learning Foundations, Problem Solving and AI Development
August 1, 2024 – June 30, 2024
UET Peshawar
B.Sc. Electrical Engineering · Electrical Engineering
August 1, 2021 – June 30, 2025
Customer Churn Prediction App (ANN + Streamlit)
January 1, 2025 – Present
Developed an Artificial Neural Network (ANN) model for customer churn prediction. Performed data preprocessing, model training, evaluation, and deployment. Built an interactive Streamlit application for real-time predictions.
LSTM-based Time Series Prediction
January 1, 2025 – Present
Designed and trained LSTM neural networks for forecasting temporal trends, applying feature engineering and optimization techniques evaluated with industry-standard metrics.
Local AI Chatbot using LangChain & Ollama
January 1, 2025 – Present
Developed a fully local AI chatbot using TinyLlama running without cloud APIs. Built conversational workflows using LangChain and integrated local LLM inference through Ollama. Designed an interactive Streamlit interface for real-time user interaction. Gained practical experience in prompt engineering and local AI deployment.
AI Chatbot with Conversation Memory
January 1, 2025 – Present
Implemented session-based conversational memory using RunnableWithMessageHistory. Built a context-aware chatbot capable of remembering previous user interactions. Integrated Groq-hosted Llama models for efficient inference and improved user experience. Explored practical applications of conversational AI and memory management.
Plant Disease Classification using Deep Learning
January 1, 2025 – Present
Developed a Convolutional Neural Network (CNN) for automated plant disease detection, training and evaluating models on labeled plant leaf image datasets to improve classification accuracy.
Speech Recognition using MFCC and KNN
January 1, 2025 – Present
Extracted Mel Frequency Cepstral Coefficients (MFCC) from speech signals and built a K-Nearest Neighbors (KNN) classifier, achieving reliable voice pattern classification through feature engineering.
Huawei Certified Career Development Associate – Artificial Intelligence (HCCDA-AI)
Huawei
June 1, 2026 – Present
AI and Machine Learning Training Programs
Unknown
June 1, 2026 – Present
Python Programming Certifications
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong passion for AI and ML, aligning well with an AI Engineer role. The diversity of personal projects, ranging from churn prediction to local chatbots and plant disease classification, indicates a broad interest and willingness to explore different facets of AI. The pursuit of certifications and bootcamps further highlights a commitment to continuous learning and professional development, which is a good cultural fit for dynamic tech environments. The lack of professional experience means cultural fit is primarily inferred from project initiative and learning drive.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work independently on complex technical challenges. The focus on personal projects suggests self-motivation and a proactive approach to learning and application. While direct team collaboration experience is not explicitly stated, the nature of AI development often requires problem-solving and iterative development, which are positive indicators. The candidate's educational background in Electrical Engineering, combined with AI bootcamps, suggests a structured approach to learning and problem-solving.