About

About

I have over 14 years of experience as a specialist in Artificial Intelligence, Machine Learning Engineering, Data Engineering and Software Engineering. During this time, I have developed complex models and applications for a range of industries, including the healthcare domain, decision-making systems in emergency response, recommendation systems, and Internet of Things (IoT) environments.

LLMs, LMMs, Agentic Systems, and Cloud AI Experience

  • Extensive hands-on experience with Large Language Models (LLMs) and Large Multimodal Models (LMMs), including Gemini, GPT-4, Mistral, Llama, and open-source transformer-based models.
  • Designed, fine-tuned, and deployed LLMs for a variety of applications: chatbots, document analysis, summarization, code generation, and multimodal reasoning (text, image, audio, video).
  • Built and orchestrated agentic AI systems using frameworks such as CrewAI, LangChain, and custom Python orchestration for multi-agent collaboration, workflow automation, and tool integration.
  • Developed advanced RAG (Retrieval-Augmented Generation) pipelines for enterprise search, knowledge management, and context-aware assistants.
  • Integrated LLMs and agentic systems with cloud platforms (Google Vertex AI, AWS Sagemaker, Azure AI, HuggingFace Inference Endpoints) for scalable, production-grade deployments.
  • Architected and managed cloud-native AI solutions using Docker, Kubernetes, and serverless technologies for robust, cost-effective, and secure operations.
  • Implemented function-calling, tool use, and API integration for real-time data retrieval, workflow automation, and external system interaction.
  • Experience with prompt engineering, evaluation pipelines, and continuous improvement of LLM-based applications for reliability, safety, and user experience.
  • Led cross-functional teams in the design and deployment of agentic and multimodal AI solutions for healthcare, enterprise automation, and research applications.

Machine Learning and Deep Learning Models

  • Extensive expertise in ML frameworks and libraries including TensorFlow, PyTorch, Pandas, Scikit-Learn, Tensorboard, OpenCV, etc.
  • Developed AI models and LLMs for text and image generation and chat applications.
  • Proficient in developing AI technologies such as generative AI models (e.g., GANs, transformers), reinforcement learning, natural language processing, CNN on Graphs, Multimodal AI models.
  • Proficient in developing cutting-edge computer vision algorithms and deep learning models for image processing, image classification and segmentation, object detection, 3D reconstruction, 3D shapes analysis, and audio/multimedia processing.

Data Engineering and Data Processing

  • Hands-on experience in data collection (structured and unstructured data), data processing (data cleaning, transformation, augmentation, etc.) workflows and feature engineering pipelines to enhance model training datasets and model evaluation techniques.
  • Experience of statistical analyzing and data visualization techniques and libraries to draw insights from data sets.
  • Gained experience with distributed computing platforms and technologies, including Apache Spark and Hadoop. Leveraged cloud-based services such as AWS and Azure to deploy and manage scalable data pipelines and AI/ML models.
  • Collaborated with cross-functional teams to understand data requirements, model objectives, and performance metrics. Translated business needs and objectives into detailed technical specifications for data pipeline development and AI model training while protecting sensitive information.

Software Developing and Programming

  • Proficiency in programming languages such as C#/C++, Python, SQL, Shell Scripts, MATLAB, and LINQ.
  • Experience with software development tools, project management tools (e.g. Jira), version control repository (e.g., TFVC, Bitbucket), code review tools (Git, GitLab), cloud platforms (e.g., AWS, Azure), containerization technologies like Docker.

Computer Vision and Image Processing

  • Deep expertise in designing and implementing computer vision algorithms for a wide range of applications, including medical imaging, industrial automation, and multimedia analysis.
  • Developed and deployed advanced models for image classification, object detection, semantic and instance segmentation, and 3D reconstruction.
  • Experience with image enhancement, denoising, super-resolution, and feature extraction techniques for both research and production environments.
  • Built multimodal AI systems that combine visual, textual, and audio data for richer understanding and decision-making.
  • Proficient in using libraries and frameworks such as OpenCV, scikit-image, Pillow, PyTorch, TensorFlow, and Keras for image and video processing.
  • Applied deep learning architectures (CNNs, GANs, transformers, Vision-Language models) to solve complex vision tasks, including medical image analysis, document understanding, and video intelligence.
  • Integrated computer vision solutions with cloud platforms and edge devices for scalable, real-time inference and analytics.

GitHub: https://github.com/khanimkh

Google Scholar: https://scholar.google.com/citations?user=eAgr8o0AAAAJ&hl=en