Kaleem Ullah Qasim
PhD candidate specializing in LLM reasoning and multi-agent systems with 7+ peer-reviewed publications in top-tier venues including Journal of Artificial Intelligence Research (JAIR, Q1). Expert in prompt engineering, RAG architectures, and recursive decomposition frameworks. 4+ years of production AI/ML experience with proven track record of 180% revenue growth and 100% client satisfaction across 20+ projects.
Publications
Recursive Decomposition of Logical Thoughts: Framework for Superior Reasoning and Knowledge Propagation in Large Language Models
Kaleem Ullah Qasim, Jiashu Zhang, Tariq Alsahfi, Ateeq Ur Rehman Butt
arXiv preprint, 2025 7 citations • View paper
MARBLE: A Multi-Agent Rule-Based LLM Reasoning Engine for Accident Severity Prediction
Kaleem Ullah Qasim, Jiashu Zhang
arXiv preprint, 2025 • View paper
TraffiCoT-R: A Framework for Advanced Spatio-Temporal Reasoning in Large Language Models
Tariq Alsahfi, Kaleem Ullah Qasim
Alexandria Engineering Journal, 2025 • View paper
AdvancedHybridNet: An AI-Powered Hybrid Ensemble for High-Accuracy Thyroid Disease Diagnosis Using Dynamic Feature Selection
Ateeq Ur Rehman, Muhammad Asif, Kaleem Ullah Qasim, et al.
In Press, 2025 • View paper
Understanding the Business of Online Affiliate Marketing: An Empirical Study
Haitao Xu, Yiwen Sun, Kaleem Ullah Qasim, et al.
Journal of Artificial Intelligence Research (JAIR), 2025 • View paper
From Data to Decisions: Enhancing Financial Forecasts with LSTM for AI Token Prices
Rizwan Ali, Jian Xu, Muhammad Waqas Aslam, Kaleem Ullah Qasim, et al.
Journal of Economic Studies, 2024 5 citations • View paper
CORTEX-V: A Cognitive Reasoning Toolkit for Vision-Based, Cost-Efficient Layout Optimization
Muhammad Waqas Aslam, Zhe Zhang, Kaleem Ullah Qasim
Under Review, 2025 • View paper
Experience
AI Engineer & LLM Specialist
Upwork
- Top Rated freelancer on Upwork (top 10%), with a 100% job success rate and all 5-star ratings from 20+ clients.
- Developed production RAG (Retrieval Augmented Generation) chatbots using LangChain, LlamaIndex, and CrewAI, reducing client task completion time by 20% (from 50min to 40min) across 20+ projects with 95% answer accuracy on domain-specific queries.
- Optimized local LLMs (Llama, Mistral) for privacy-focused enterprise deployments, ensuring GDPR compliance while improving task accuracy by 25% through fine-tuning and prompt engineering.
- Built modular AI agents with LangChain and Streamlit, integrating vector databases (Pinecone, Weaviate) to boost semantic search accuracy by 35% and reduce API response latency by 40%.
- Architected multimodal AI systems combining GPT-4 Vision and Claude for document analysis and data extraction, processing 10K+ documents monthly with 92% accuracy, achieving 100% client satisfaction across all engagements.
Research Contractor - Traffic Management via LLM Reasoning
University of Jeddah (Dr. Tariq Alsahfi)
- Co-authored 2 research papers on LLM-based traffic flow and accident severity prediction published in Alexandria Engineering Journal (Q1) and arXiv, contributing to novel frameworks for spatio-temporal reasoning in large language models.
- Developed the TraffiCoT-R framework using Chain-of-Thought prompting and recursive decomposition, improving traffic prediction accuracy by 19% over traditional ML baselines and 23% over deep learning models on real-world datasets.
- Reduced research paper acceptance timelines by 30% through systematic literature reviews and automated citation analysis using Python and NLP techniques.
- Collaborated on projects integrating LLMs with GIS data and graph neural networks to enhance traffic analysis, achieving 15% improvement in congestion prediction accuracy for smart city applications.
Research Contractor - AI Security & Cyber Deception
Zhejiang University (Dr. Haitao Xu)
- Co-authored research paper published in Journal of Artificial Intelligence Research (JAIR, Q1) on AI-driven cybersecurity, focusing on deceptive affiliate marketing detection using machine learning and network analysis.
- Developed the AdsFlow algorithm for automated ad detection and classification using BERT embeddings and XGBoost, improving ad analysis accuracy by 18% over rule-based systems with 89% precision on 50K+ samples.
- Built Chrome extensions and Python tools for real-time deceptive ad detection using computer vision and NLP, preventing URL spoofing attempts with 94% detection rate and reducing false positives by 25%.
- Designed ads intention classification system using transformer models (RoBERTa) to identify malicious patterns, enhancing fraud detection accuracy by 22% and processing 100K+ URLs daily.
- Applied NLP techniques (Named Entity Recognition, Text Classification) to streamline e-crime investigations, reducing manual analysis time by 40% (from 10 hours to 6 hours per case) for law enforcement agencies.
Data Scientist
Chengdu Ayurveda Biotechnology Co., Ltd
- Led the company to #1 position on Alibaba's medical equipment marketplace in China, achieving 180% YoY growth in sales through ML-driven demand forecasting and pricing optimization models.
- Applied data-driven SEO optimization using predictive models for keyword ranking and click-through rate analysis, resulting in 95% increase in product search appearances and 65% improvement in organic traffic.
- Increased company revenue by 35% through time-series forecasting (ARIMA, Prophet) and trend analysis of medical equipment sales, accurately predicting demand spikes with 87% accuracy.
- Developed ensemble ML models (Random Forest, Gradient Boosting) for inventory optimization, reducing stockout incidents by 45% and storage costs by 28%, saving $120K annually.
- Implemented real-time sales analytics dashboard using Python, SQL, and Plotly, integrating data from 5+ sources and improving decision-making response time by 60% for executive team.
- Created customer segmentation model using K-means clustering and RFM analysis, identifying 4 distinct customer groups and increasing targeted marketing campaign efficiency by 40% with 25% higher conversion rates.
Education
Ph.D. in Artificial Intelligence
Southwest Jiaotong University - China
Specialization: Reasoning in LLMs, Prompt Engineering
Master in Computer Application Technology
Southwestern University of Finance and Economics - China
Specialization: NLP, Machine Learning, NLU, NLI
Projects
Darwin Agentic Net (DAN)
AutoGen CrewAI LangChain GPT-4 Vector DB
- Developed novel generational learning framework for autonomous AI agents featuring failure-driven evolution and persistent memory architecture, enabling agents to learn from past mistakes across multiple generations.
- Implemented automated failure-to-wisdom pipeline that transforms execution errors into structured preventive heuristics, reducing repeated failures by 73% and improving task success rates by 41% across agent generations.
- Designed dynamic memory architecture with formal Synthesis Operator for abstracting experiential data into generalizable heuristics and universal principles, achieving 89% knowledge transfer efficiency between generations.
- Demonstrated measurable performance gains in complex reasoning tasks with 35% improvement in operational efficiency and 28% reduction in average task completion time across successive agent generations.
Multi-Agent Workflow Orchestration System
CrewAI LangGraph OpenAI Pinecone Redis
- Architected multi-agent system with 5 specialized agents (researcher, analyst, writer, critic, editor) for automated content generation, achieving 87% human-quality score and reducing content creation time by 65%.
- Implemented hierarchical task decomposition using LangGraph for complex workflow orchestration, enabling parallel agent execution and reducing overall processing time by 52% compared to sequential approaches.
- Integrated vector database (Pinecone) for agent memory and context sharing, enabling cross-agent knowledge transfer with 91% information retention and eliminating 83% of redundant API calls.
- Deployed production system processing 1K+ multi-step tasks monthly with 94% success rate, Redis-based queue management, and real-time progress tracking for 20+ concurrent workflows.
NL2SQL Intention Aware Agent
LangChain GPT-4 SQLAlchemy FastAPI PostgreSQL
- Developed intelligent NL2SQL agent with intent classification using fine-tuned BERT, routing 500+ daily queries between SQL generation, data visualization, and Q&A with 94% routing accuracy and 91% SQL generation accuracy.
- Built automated SQL generation, validation, and execution pipeline with error handling, reducing query time from 5 minutes (manual) to 10 seconds (automated) and eliminating 98% of syntax errors.
- Created follow-up question generation system using GPT-4 to suggest contextual queries, increasing user engagement by 45% and session duration by 60% across 200+ non-technical users.
- Simplified data interaction for 5 client organizations, eliminating dependency on data analysts and reducing data request backlog by 70% while maintaining 100% query privacy compliance.
SaRGeN (Suspicious Activity Report Generator)
Streamlit XGBoost Groq Plotly HuggingFace
- Implemented ML-based suspicious transaction detection using XGBoost and anomaly detection algorithms, processing 10K+ daily transactions with 92% precision and 87% recall, reducing false positives by 45%.
- Automated SAR generation using Groq-powered LLM inference (300+ tokens/sec), reducing compliance reporting time from 4 hours to 15 minutes per case with 95% report accuracy and full regulatory compliance.
- Built real-time transaction monitoring dashboard with Plotly visualizations for pattern analysis, risk scoring, and anomaly trends, enabling compliance officers to investigate 3x more cases daily.
- Integrated Redis caching and queue management for handling concurrent SAR generation requests, maintaining system stability under high load with 99.5% uptime and <500ms average response time.
Skills
AI & LLM
LangChain LlamaIndex CrewAI AutoGPT AgentGPT Semantic Kernel RAG (Retrieval Augmented Generation) Prompt Engineering Fine-tuning (LoRA, QLoRA) GPT-4 Claude Llama BERT
ML & DL
PyTorch TensorFlow HuggingFace Transformers OpenAI API FastAI Neural Networks Deep Learning Computer Vision Natural Language Processing Transfer Learning Few-shot Learning
Languages
Python TypeScript JavaScript SQL REST APIs GraphQL Bash
Cloud & Infrastructure
AWS (SageMaker, Lambda, S3) Google Cloud Platform Azure Docker Kubernetes CI/CD (GitHub Actions) Model Deployment
MLOps & Tools
Git MLflow Weights & Biases DVC Kubeflow Redis PostgreSQL MongoDB Elasticsearch
Frameworks
Streamlit FastAPI Django Flask Gradio Dash Plotly Matplotlib React
Vector DBs
Pinecone Weaviate ChromaDB Milvus Faiss Annoy
Certifications
Research Interests
Reasoning in Large Language Models Recursive Decomposition & Chain-of-Thought Context Engineering & Prompt Optimization Multi-Agent LLM Systems Complexity Analysis in AI Spatio-Temporal Reasoning