Summary
Hafiz N. is a Generative AI Engineer with 2+ years of hands-on experience designing and deploying end-to-end large language model (LLM) systems and multimodal AI solutions for real-world applications. He specializes in building advanced RAG architectures, including Hybrid GraphRAG and Vector RAG systems that combine Neo4j knowledge graphs with vector databases such as Azure AI Search, FAISS, and ChromaDB to enhance semantic reasoning and factual accuracy. His expertise spans LangChain, LangGraph, FastAPI, Flask, and Hugging Face ecosystems, enabling the development of scalable conversational AI agents, multilingual voice assistants, automation pipelines, and structured data generation systems. Hafiz has fine-tuned Qwen-based models for JSON and text-to-SQL generation, engineered multimodal chatbots, and integrated Azure OpenAI, Document Intelligence, and Blob Storage into enterprise-grade GenAI platforms. With strong foundations in Python, PyTorch, TensorFlow, and MLOps practices using Docker, he consistently delivers low-latency, production-ready AI systems that transform complex structured and unstructured data into intelligent, automated workflows.
Meet hafiz N.
Assessment Scores
Great score on Implement LRU Cache Algorithm, Difficulty Medium
Great score on Understanding Rate Limiting in APIs, Difficulty Medium
Great score on Ensuring Real-time Data Accuracy in Mobile App, Difficulty Medium
Great score on Reverse Digits of a 32-bit Integer, Difficulty Easy
Great score on Word Search in Grid, Difficulty Medium
Great score on Validate Parentheses in a String, Difficulty Hard
Experience
Chainfore
Generative AI Engineer
Full-Time
Nov 2025 — Present
Designed and implemented a production-grade Hybrid RAG system (GraphRAG + Vector RAG) to improve semantic understanding and factual accuracy on structured data (XLSX, CSV, tables, financial datasets).
Built end-to-end data ingestion and retrieval pipelines, combining Neo4j (Graph DB) for entity-relationship reasoning with Azure AI Search (Vector DB) to overcome limitations of traditional RAG.
Utilized Azure OpenAI, Azure Document Intelligence, Azure AI Search, and Azure Blob Storage to deliver a scalable, low-latency, enterprise-ready GenAI chatbot for Auxee v2.
Technologies:
Azure OpenAI
Azure Document Intelligence
Azure AI Search
Azure Blob Storage
Neo4j
Azure AI Search
RapidsAI
Generative AI Engineer
Full-Time
Jun 2024 — Nov 2025
Engineered a scalable AI intelligence platform featuring RAG chatbot, summarizer, and Multimodal Analysis Modules, containerized with Docker and integrated via Flask APIs to provide unified, intelligent data interaction.
Delivered AI-driven automation for Supraz by building a Multi-modal RAG chatbot that replaced manual parts lookup with instant, query-based access to automobile data, significantly improving productivity and data accessibility.
Engineered an intelligent Multilingual voice-based customer service agent leveraging conversational AI to manage inbound calls, explain dental services, schedule appointments, and send confirmation emails, delivering a seamless and automated booking experience.
Engineered a real-time weather agent for weatherwalay.com using the Weatherwalay API, rule-based prompt engineering with LLM tool-calling, and LangGraph orchestration, delivering now-casts and forecasts with 95% accuracy.
Designed and fine-tuned a suite of Qwen-based large language models (0.5B–7B) for structured data generation, optimizing them to produce valid JSON and SQL outputs from unstructured text using Salesforce/xlam-function-calling and GretelAI text-to-SQL datasets.
Engineered a comprehensive n8n workflow for automated lead engagement, encompassing lead generation, deep research, persona creation, personalized email drafting with LLMs, and follow-up sequences.
Developed an AI-driven video generation and social media automation agent using n8n that creates and uploads daily videos across nine social media platforms, streamlining content distribution and boosting online engagement.
Technologies:
RAG chatbot
Docker
Flask APIs
Weatherwalay API
LLM
Salesforce
GretelAI
n8n
Business Incubation Center
Machine Learning Engineer
Contract
Jan 2024 — Jun 2024
Built and deployed machine learning models for predictive analytics, improving decision-making accuracy for early-stage startups by 35%.
Developed data preprocessing pipelines and feature engineering workflows using Python and scikit-learn, reducing model training time by 40%.
Technologies:
Python
scikit-learn
Projects
Delivered AI-driven automation for Supraz by building a Multi-modal RAG chatbot that replaced manual parts lookup with instant, query-based access to automobile data, significantly improving productivity and data accessibility
Agentic AI Plateform
Engineered a scalable AI intelligence platform featuring RAG chatbot, summarizer, and Multimodal Analysis Modules, containerized with Docker and integrated via Flask APIs to provide unified, intelligent data interaction.
Education
Bachelor of Science in Computer Science
2020 - 2024
University Of Gujrat
Gujrat, Pakistan
