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.
AI Engineer
Full-time available
Bio
Hafiz M. is a highly skilled AI/ML and Computer Vision Engineer with hands-on experience in generative AI, agentic systems, and Retrieval-Augmented Generation (RAG) pipelines. With a BS in Software Engineering from Riphah International University and additional certification in Generative AI using LangChain and HuggingFace, Hafiz brings strong technical and practical expertise. His professional journey at Xpl Services includes roles as Associate AI Engineer, Junior AI Engineer, and AI Intern, where he has collaborated with…
Expertise
PythonGitNumPyPyTorchFast API
Video Intro
Assessments
Projects
Experience
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 ImagingJunior 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
pythonlangcahintensorflowpandasnumpyAI Intern
Xpl Services· AI/MLJan 2025 — Mar 2025- Built Machine Learning models
YoloPandasNumpysciket learn
Education
BS Software Engineering
Riphah International UniversityFaisalabad, Pakistan2021 — 2025Generative AI Course
UdemyIndia2025 — 2025
AI Engineer
Full-time available
Hafiz is available for hire
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Bio
Hafiz M. is a highly skilled AI/ML and Computer Vision Engineer with hands-on experience in generative AI, agentic systems, and Retrieval-Augmented Generation (RAG) pipelines. With a BS in Software Engineering from Riphah International University and additional certification in Generative AI using LangChain and HuggingFace, Hafiz brings strong technical and practical expertise. His professional journey at Xpl Services includes roles as Associate AI Engineer, Junior AI Engineer, and AI Intern, where he has collaborated with…
- Projects completed4
- Hourly rate
- 0-2 years experience
- Pakistan
- Member since Mar 2026
Industries
AI/ML
Expertise
PythonGitNumPyPyTorchFast API
Video Intro
Assessments
Projects
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.
Experience
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 ImagingJunior 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
pythonlangcahintensorflowpandasnumpyAI Intern
Xpl Services· AI/MLJan 2025 — Mar 2025- Built Machine Learning models
YoloPandasNumpysciket learn
Education
BS Software Engineering
Riphah International UniversityFaisalabad, Pakistan2021 — 2025Generative AI Course
UdemyIndia2025 — 2025
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