I'm an AI Tech Lead with a passion for building production-grade AI systems that solve real-world problems. I specialize in architecting and deploying LLM-powered solutions, multi-agent systems, and enterprise-scale RAG pipelines that deliver measurable business impact.

class ZubairAshfaque:
    def __init__(self):
        self.role = "AI Tech Lead"
        self.company = "Medical Guardian"
        self.focus_areas = [
            "LLM Engineering & Fine-tuning",
            "Multi-Agent AI Systems",
            "Retrieval-Augmented Generation (RAG)",
            "Deep Learning & Neural Networks",
            "Healthcare AI & Predictive Analytics",
            "MLOps & Production Deployment"
        ]
        self.current_mission = "Building AI-first products that improve patient care"

    def get_expertise(self):
        return {
            "AI_Agents": ["AutoGen", "CrewAI", "LangGraph", "Semantic Kernel"],
            "LLM_Stack": ["Azure OpenAI", "AWS Bedrock", "LangChain", "LlamaIndex"],
            "RAG_Systems": ["Vector DBs", "Azure AI Search", "Pinecone", "FAISS", "Chroma"],
            "Deep_Learning": ["TensorFlow", "PyTorch", "Keras", "Transformers"],
            "Cloud_AI": ["Azure ML", "Azure AI Studio", "AWS Bedrock", "Google Cloud AI"],
            "Data_Science": ["Scikit-learn", "XGBoost", "Time Series", "NLP"]
        }

• 🤖 Lead AI/ML teams to build LLM-powered, AI-first products for healthcare
• 🧠 Architect multi-agent ecosystems using AutoGen, LangChain, and Semantic Kernel
• 📚 Design enterprise RAG pipelines integrating vector databases and knowledge graphs
• 🏥 Develop predictive models for patient risk stratification and readmission prediction
• ⚡ Deploy production AI systems on Azure ML, AWS, and cloud-native platforms
• 🔬 Drive innovation in Responsible AI, governance, and compliance (HIPAA, SOC2, GDPR)

• 🎯 20% reduction in patient readmission rates through predictive analytics models
• 🤖 Built Agent Nurse Bot with Agentic RAG system for clinical staff decision support
• 💊 Improved patient safety by 15% with drug interaction prediction ML models
• 📞 Achieved 10% increase in customer satisfaction using NLP-powered sentiment analysis
• 🔍 50% faster data lookup times with AWS Bedrock multimodal RAG solution

• 💵 $0.8M annual profit increase through customer churn prediction models
• 📊 15% boost in customer retention using predictive attrition models
• 🛡️ 20% reduction in revenue leakage via anomaly detection systems
• 📈 20% increase in product adoption through behavioral analytics
• 🎯 95% compliance adherence tracking with ML monitoring models

• 🏗️ Architected enterprise-scale RAG pipelines using Azure AI Search, Pinecone, and FAISS
• 🤝 Deployed multi-agent systems with AutoGen, CrewAI, and Semantic Kernel
• 🧪 Implemented Responsible AI frameworks ensuring HIPAA, SOC2, GDPR compliance
• ⚡ Built real-time ML pipelines processing 1M+ files with Ray and Dask
• 🎬 Integrated multimodal AI processing videos, documents, and knowledge bases

NLP • Streamlit • Real-time Classification

Interactive web application for real-time sentiment analysis using Naive Bayes algorithm. Features an intuitive Streamlit interface with live text classification and model evaluation metrics.

Python NLP Scikit-learn Streamlit Text Analytics

Classification • Feature Engineering • Model Comparison

End-to-end machine learning solution for predicting income inequality using census data. Comprehensive feature engineering, model comparison, and interpretability analysis.

Python Scikit-learn Pandas Data Visualization Classification

MLOps • Model Deployment • Production Systems

Production-ready deployment of Road Traffic Accident prediction model demonstrating MLOps best practices, containerization, and REST API development.

Python Flask/FastAPI Docker ML Deployment CI/CD

Time Series • ARIMA • Prophet • LSTM

Comprehensive time series analysis covering statistical methods (ARIMA), machine learning (Prophet), and deep learning (LSTM) for forecasting applications.

Python ARIMA Prophet LSTM Statsmodels TensorFlow

AutoML • Hyperparameter Tuning • Model Stacking

Leveraging H2O.ai's automated machine learning platform for efficient model development, hyperparameter optimization, and ensemble methods.

Python H2O.ai AutoML Model Stacking Feature Engineering

Statistics • Hypothesis Testing • Business Analytics

Statistical analysis framework for A/B testing with hypothesis testing, statistical significance evaluation, and actionable business insights.

Python Statistics A/B Testing Pandas Hypothesis Testing

Apr 2025 – Present

• Leading cross-functional AI/ML teams building LLM-powered healthcare products
• Architecting multi-agent ecosystems using AutoGen, LangChain, and Semantic Kernel
• Deploying enterprise RAG solutions with Azure AI Search and vector databases
• Implementing Responsible AI governance for HIPAA-compliant deployments

Nov 2024 – Mar 2025

• Built Agent Nurse Bot with Agentic RAG for clinical decision support
• Deployed AWS Bedrock multimodal RAG integrating videos, documents, and knowledge bases
• Developed predictive analytics for patient readmission risk (20% reduction achieved)

Jan 2023 – Oct 2024

• Created customer churn prediction models ($0.8M annual profit increase)
• Built compliance monitoring using Random Forest and Time Series Analysis
• Developed fraud detection systems (20% reduction in revenue leakage)

Certifications:

• 🏆 TensorFlow Developer - TensorFlow.org
• 📊 Google Data Analytics - Google

Education:

• 🎓 Bachelor in Telecom Systems - Beaconhouse National University

Current Focus:
  - Agentic AI: Multi-agent orchestration and autonomous systems
  - Advanced RAG: Graph RAG, Hybrid Search, Re-ranking strategies
  - LLM Fine-tuning: PEFT, LoRA, Instruction tuning
  - Multimodal AI: Vision-Language models, Document understanding
  - AI Governance: Responsible AI, bias detection, explainability

Exploring:
  - Reinforcement Learning from Human Feedback (RLHF)
  - Neural Architecture Search (NAS)
  - Federated Learning for Healthcare
  - Edge AI and Model Compression

I'm passionate about:

• 🚀 Building production-grade AI systems that solve real problems
• 🤝 Contributing to open-source AI/ML projects
• 📚 Sharing knowledge through technical writing and mentoring
• 💡 Exploring cutting-edge research in LLMs and AI Agents

"Building AI systems that make a difference, one algorithm at a time."

⭐️ From Zubair Ashfaque | AI Tech Lead | LLM & RAG Specialist