AI Engineer

Open to full-time

Bio

Hafiz M. is an AI Engineer with several years of experience in AI/ML and computer vision. His expertise lies in generative AI, agentic systems, and Retrieval-Augmented Generation (RAG) pipelines. Proficient in Python, PyTorch, TensorFlow, and Keras, Hafiz builds scalable AI pipelines and high-performance computer vision systems. He is skilled in deploying solutions using FastAPI and REST APIs, and utilizes modern AI technologies such as LLMs, LangChain, and RAG systems. His work involves fine-tuning large language models and leveraging GPU clusters for efficient training and inference. At Xpl Services, Hafiz developed an AI-powered brain tumor analysis system using 3D U-Net for precise segmentation, and a SmartCV Assistant for resume analysis with semantic retrieval. His projects highlight significant achievements in medical imaging and multimodal AI systems, demonstrating his ability to deliver impactful AI solutions. Hafiz holds a BS in Software Engineering from Riphah International University and a certification in Generative AI. He is well-suited for roles that involve cutting-edge AI research and development, particularly in intelligent and human-centered systems.

Expertise

PythonGitNumPyPyTorchFast API

Video Intro

Assessments

General Project Assessment

(88/100)

Great on Find the Length of the Longest Substring Without Repeating CharactersEasy
Great on LLM Integration Producing Inconsistent OutputsMedium
Great on Evaluate and Improve RAG Chatbot Response QualityMedium

Projects

AI-Powered Brain Tumor Analysis System

End-to-end system using 3D U-Net (BraTS 2020) for precise tumor sub-region segmentation (necrotic core, edema, enhancing tumor). RAG pipeline with Groq’s Llama-3.3-70B and Llama-4-Scout for patient-specific clinical reports. Streamlit interface for input, visualization, and PDF report export.

Associate AI Engineer
Xpl Services· AI/MLJun 2025 — Mar 2026
- Collaborated with cross-functional international teams to design, train, and deploy scalable production-grade AI pipelines
- Fine-tuned state-of-the-art LLMs (LLaMA, Mistral, Qwen, GPT-OSS) for domain-specific applications using efficient distributed training
- Designed and implemented agentic AI workflows and orchestration layers using LangGraph and FastAPI
- Built and productionized Retrieval-Augmented Generation (RAG) systems leveraging LangChain, Qdrant, FAISS, and Pinecone vector databases
- Developed high-performance computer vision pipelines (YOLO, U-Net, Attention U-Net, OpenCV) for object detection, medical imaging, and surveillance applications
- Automated end-to-end AI workflows through multi-agent systems integrated into production REST APIs
- Implemented Model Context Protocol (MCP) to enable dynamic tool use and external knowledge integration in LLM-based agents
- Utilized Lambda Labs H100 GPU clusters for large-scale model training, fine-tuning, and inference deployment
- Engineered multimodal AI systems combining vision, text, and reasoning capabilities for complex real-world tasks
PythonGitNumPyPandasScikit-LearnPyTorchTensorFlowKerasCNNLSTMRNNNLPLLMsLangChainLangGraphRAGLoRAUnslothYOLOU-NetOpenCVMedical Imaging
Junior AI Engineer
Xpl Services· AI/MLMar 2025 — May 2025
- Trained and Fine-Tuned Large language models
- Built Data pipelines for LLMs
- Worked On Retrieval Augmented Generation systems
pythonlangcahintensorflowpandasnumpy
AI Intern
Xpl Services· AI/MLJan 2025 — Mar 2025
- Built Machine Learning models
YoloPandasNumpysciket learn