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Generative AI Engineer with less than a year in Data Science & Machine Learning
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
I'm a Computer Science graduate specializing in Data Science, Machine Learning, and Generative AI. I build production systems in Python, developing data pipelines and deploying multi-modal AI applications. My experience spans academic research and industry engineering, where I've shipped real-world data solutions and AI products. I'm targeting data science and ML engineering roles.
University of Education Lahore
Bachelor · Computer Science
August 1, 2021 – June 30, 2025
Automotive Artificial Intelligence (AAI) GmbH
Generative AI Engineer (Intern)
June 1, 2025 – January 1, 2026
India
CORA - Compliance & Regulatory Assistant
October 1, 2025 – January 1, 2026
• Combined deterministic processing with LLMs and VLMs to automate data extraction and structuring, applied each approach based on document patterns identified. • Conducted data interpretation and exploratory analysis to inform stakeholder decisions on pipeline design, data coverage, and quality thresholds across diverse regulatory document types. • Developed a document comparison feature that shows word-level differences between versions of the same regulation across the corpus. • Built the data infrastructure for a regulatory knowledge graph that connects related documents and enables better navigation and analysis.
Emotionally Aware Multilingual Voice Chatbot
June 1, 2025 – July 1, 2025
• Developed a real-time voice chatbot capable of understanding emotion, language, and intent for mental health support. • Integrated STT, BERT-based emotion detection, Vertex AI (Gemini), and expressive TTS over a FastAPI + WebSocket backend for natural, low-latency conversations.
Research: EEG-HRV Patterns across Sleep Stages
November 1, 2024 – March 1, 2025
• Conducted research on sleep stage classification and autonomic activity patterns using EEG and ECG data from the SHHS dataset (6,441-participant NHLBI multi-center study). • Restored usability of HRV feature by detecting sensor malfunction outliers (max: 58M vs. 99.7th percentile: ~40) validated as physiologically impossible; removed 0.03% of records, reducing standard deviation from 147,483 to 2.45 (~100% reduction). • Trained ensemble and kernel-based classifiers (Random Forest, SVM, XGBoost) for sleep stage prediction, reaching 81% accuracy on challenging physiological datasets; documented findings in technical report. • Developed an interactive application for sleep stage prediction with two input modes: uploaded patient signal files (with built-in analytics) and direct entry of EEG/ECG feature values via sliders
Cultural Fit Analysis
The candidate's projects showcase a diverse range of applications, from biomedical research to regulatory compliance and mental health support chatbots, indicating adaptability and a broad interest in AI's impact. The internship at Automotive Artificial Intelligence (AAI) GmbH aligns well with industry application of AI. The academic background combined with practical project work suggests a proactive and learning-oriented individual. The target role of 'Generative AI Engineer' is a strong match for the candidate's demonstrated skills and project focus.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills, particularly in data cleaning and feature engineering (e.g., HRV feature restoration). Project descriptions indicate an ability to work on complex, multi-faceted problems and deliver functional applications. The internship experience suggests an understanding of production-level challenges like scalability and data quality. However, without direct assessment, collaboration and stress handling cannot be fully evaluated.