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AI Engineer passionate about building, training, and evaluating ML models across supervised and unsu
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Passionate Machine Learning enthusiast seeking an AI/ML internship to apply practical skills in building, training, and evaluating models across supervised and unsupervised learning domains. Experienced in developing end-to-end ML pipelines using Python and its data science ecosystem. Eager to contribute to data-driven solutions, experiment with real-world datasets, and grow within a research-focused or applied ML engineering environment.
FAST National University of Computer and Emerging Sciences, Islamabad
BS Software Engineering
August 1, 2023 – Present
Concordia Colleges, DHA Campus, Islamabad
FSc. Pre-Engineering
June 1, 2021 – May 31, 2023
Smart Mental Health Chat & Emotion-Aware Support System
June 1, 2026 – Present
Built emotion detection model to classify user sentiment and route messages to context-appropriate support responses. Designed multi-turn conversation pipeline with state tracking and escalation logic for high-risk inputs.
Traffic Intersection & Parking Simulation
June 1, 2026 – Present
Engineered multi-threaded vehicle simulation with priority scheduling for emergency vehicles using POSIX threads. Implemented IPC between traffic controller processes via UNIX pipes; managed shared state with bounded semaphores for parking capacity.
DocIQ Multi-Modal Document Intelligence System
June 1, 2026 – Present
Architected a full-stack RAG pipeline: ingestion → chunking → vector embedding → semantic retrieval → LLM generation. Integrated pdfplumber and Tesseract OCR for multi-format document parsing; enabled cross-document QA with source attribution. Applied sentence-transformers for dense embeddings stored in ChromaDB; implemented cosine-similarity semantic search at scale. Exposed document intelligence via FastAPI REST endpoints with async processing support.
ML Portfolio Recommender, Classifiers & Prediction Models
June 1, 2026 – Present
Developed collaborative-filtering Movie Recommender System using cosine-similarity on a user-item matrix. Built Email Spam Classifier and Fake News Detector using TF-IDF features with Naive Bayes / Logistic Regression. Implemented regression pipelines for Diabetes Risk Prediction and House Price Estimation with feature engineering and cross-validation.
Algorithmic Games & Simulations
June 1, 2026 – Present
Zombie Survival Game: applied graph traversal, queues, and linked lists for pathfinding and entity management (DSA). Risk Board Game rendered with OpenGL (GLEW/GLUT); Tetris clone with SFML; Brick Breaker in MASM 8086 using Irvine32.
Forest Fire Weather Index Predictor
June 1, 2026 – Present
Built an end-to-end ML regression model to predict the Fire Weather Index (FWI) using the Algerian Forest Fires dataset. Applied data preprocessing, EDA with Pandas/Seaborn, and trained Ridge Regression model with hyperparameter tuning via cross-validation. Developed a Flask web application for real-time FWI prediction and deployed it on Railway cloud platform.
Smart Disaster Response MIS
June 1, 2026 – Present
Designed normalized relational schema for multi-agency disaster coordination; built query-driven dashboard for resource and incident tracking.
Python Machine Learning: Beginner to Pro
Udemy
June 1, 2026 – Present
Complete Python Course: Beginner to Advanced
Udemy
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio demonstrates a strong interest in applying AI/ML to solve real-world problems, aligning well with an AI Engineer role. The diversity of projects, from document intelligence to mental health support and disaster response, indicates a broad interest and willingness to tackle different domains. The focus on personal projects and continuous learning (certifications) suggests a self-starter mentality. However, the lack of team-based projects or professional experience makes it challenging to assess collaboration and cultural integration fully.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and problem-solving attitude, with an interest in exploring advancements in AI research and systems programming. The ability to work on diverse projects, from ML models to system simulations, suggests adaptability and a broad technical curiosity. However, without direct work experience or psychometric test results, it's difficult to fully assess stress handling, team collaboration, or specific work attitudes.