Haseeb Javed. Ph.D.

Generative AI Engineer | Large Language Model (LLM) Specialist

Suwon, KR.

About

Highly accomplished Ph.D. graduate researcher with deep expertise in Generative AI, specializing in designing and optimizing Large Language Model (LLM) pipelines, Retrieval-Augmented Generation (RAG) frameworks, and Agentic AI architectures. Proven ability to develop computationally efficient AI systems, enhance model performance, and drive scalable deployment in real-world ML applications, making me an ideal candidate for a Generative AI Engineer role in Korea.

Work

Sungkyunkwan University (SKKU)

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Teaching Assistant – Computing and Informatics Faculty

Suwon, South Korea, Korea (Republic of)

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Summary

Supported undergraduate and graduate-level courses in computing and informatics, facilitating student learning and project development.

Highlights

Assisted in undergraduate and graduate-level courses, focusing on algorithms, data structures, and ML foundations for over 100 students.

Mentored students on practical programming assignments, research methodologies, and project implementations, enhancing their technical proficiency and academic success.

Sungkyunkwan University (SKKU)

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Researcher

Suwon, South Korea, Korea (Republic of)

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Summary

Led advanced research projects in machine learning and deep learning, specializing in model robustness and explainability to drive innovation in AI applications.

Highlights

Spearheaded research in machine learning and deep learning, focusing on model robustness and explainability, leading to the development of novel AI solutions.

Designed and developed neural network models for early diagnosis of patient diseases using medical imaging and structured prediction techniques, improving diagnostic accuracy.

Collaborated with interdisciplinary teams to optimize algorithms, resulting in multiple published research papers in high-impact journals (e.g., MDPI Energies, Sensors).

Contributed to the advancement of GenAI methods in research-driven systems, emphasizing interpretability, efficiency, and scalable deployment.

Muhammad Nawaz Sharif University of Engineering & Technology (UET Multan)

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Research Assistant

Multan, Pakistan, Pakistan

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Summary

Conducted research on energy systems and AI integration, contributing to sustainable infrastructure and smart grid solutions.

Highlights

Conducted research under the supervision of an HEC-approved supervisor, focusing on energy systems and AI integration, contributing to sustainable solutions.

Published over six peer-reviewed articles in high-impact journals, including MDPI Energies (Impact Factor 3.004) and Sensors (3.8), achieving a total impact factor of 40+.

Contributed to multi-disciplinary projects that leveraged AI-based solutions for sustainable infrastructure and smart grid systems, enhancing system efficiency and reliability.

Education

SUNGKYUNKWAN UNIVERSITY

Suwon, , Korea (Republic of)
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PhD Computer Science and Engineering Grade: CGPA: 4.25

Courses

Research Focus: Developing computationally efficient AI systems with emphasis on Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI architectures, optimizing model performance, scalability, and interpretability across real-world ML applications.

Technical Proficiency: Advanced expertise in Python (PyTorch, TensorFlow, Scikit-learn), along with Java, C++, and R, for implementing end-to-end ML/LLM pipelines, fine-tuning (LoRA/PEFT), embedding-based retrieval systems, and scalable data processing workflows.

Applied AI & Modeling: Designed dynamic ensemble frameworks, high-dimensional statistical models, and retrieval-integrated learning systems to extract structured insights from large-scale data, enhancing predictive accuracy, robustness, and decision intelligence.

MUHAMMAD NAWAZ SHARIF UNIVERSITY OF ENGINEERING & TECHNOLOGY (UET MULTAN)

Multan, , Pakistan
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Master of Science Electrical Engineering Grade: CGPA: 3.613

Courses

Expertise in designing energy management systems using ML algorithms for microgrids, incorporating demand-response strategies.

MUHAMMAD NAWAZ SHARIF UNIVERSITY OF ENGINEERING & TECHNOLOGY (UET MULTAN)

Multan, , Pakistan
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Master Project Management Grade: CGPA: 3.791

Courses

Specialized in Agile and project management methodologies, focusing on resource allocation optimization and risk mitigation strategies.

UNIVERSITY OF ENGINEERING & TECHNOLOGY (UET TAXILA)

Taxila, , Pakistan
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Bachelor of Science Electrical Engineering

Publications

Robustness in Deep Learning Models for Medical Diagnostics: Security and Adversarial Challenges Towards Robust AI Applications

Research on ensuring the security and robustness of deep learning models in medical diagnostics against adversarial challenges.

Impact of AI and Dynamic Ensemble Techniques in Enhancing Healthcare Services: Opportunities and Ethical Challenges

Exploration of how AI and dynamic ensemble techniques can enhance healthcare services, addressing both opportunities and ethical considerations.

Ethical Frameworks for Machine Learning in Sensitive Healthcare Applications

Development of ethical frameworks for deploying machine learning models in sensitive healthcare applications to ensure responsible AI.

An Ensemble Stacking Algorithm to Improve Model Accuracy in Bankruptcy Prediction

Implementation of an ensemble stacking algorithm to significantly improve the accuracy of bankruptcy prediction models.

A Comparative Analysis of Artificial Intelligence and Machine Learning Approach to Estimate Currents in Electrical Power Transmission Lines

Comparative study of AI and ML approaches for estimating currents in electrical power transmission lines, optimizing grid management.

Certificates

MACHINE LEARNING

Standford University (Coursera)

DEEP LEARNING (5 COURSES SPECIALIZATION)

DeepLearning.AI (Coursera)

PYTHON FOR EVERYBODY (4 COURSES SPECIALIZATION)

University of Michigan (Coursera)

Skills

Programming & Development

Python, PyTorch, TensorFlow, Scikit-learn, NumPy, Pandas, C++, Java, R, MATLAB.

Generative AI & Machine Learning

Large Language Models (LLMs), LoRA/PEFT Fine-Tuning, Retrieval-Augmented Generation (RAG) Systems, Agentic AI Architectures, Multi-Step Reasoning, Dynamic Ensemble Learning, Statistical Modeling, NLP, Topic Modeling, Text Classification, Deep Learning, Neural Networks, Hyperparameter Tuning, Regularization, Convolutional Neural Networks, Sequence Models.

Data & Analytics Tools

Data Preprocessing, Feature Engineering, Model Evaluation, Web Analytics, Data Visualization, Python Data Structures, Accessing Web Data.

Productivity & Documentation

Microsoft 365, Word, Excel, PowerPoint.

Project Management

Agile Methodologies, Resource Allocation Optimization, Risk Mitigation Strategies.

Projects

Agentic AI Systems & Autonomous LLM Architectures

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Summary

Designed and developed agent-based AI systems capable of multi-step reasoning, tool integration, memory management, and adaptive decision-making.

Retrieval-Augmented Generation (RAG) & Knowledge-Grounded LLMs

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Summary

Developed advanced RAG pipelines integrating embedding models, vector databases, and hybrid retrieval strategies.

Parameter-Efficient LLM Adaptation & Optimization

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Summary

Conducted research on LoRA/PEFT-based fine-tuning, domain adaptation, quantization (INT8/INT4), and computational efficiency techniques.

Trustworthy & Interpretable Generative AI

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Summary

Focused on improving transparency, uncertainty estimation, and controllability in LLM-driven systems for high-stakes applications.