Ali Karimi

About Ali Karimi

AI and Machine Learning leader with 12+ years of experience architecting and deploying computer vision, machine learning, generative AI, and advanced analytics solutions. Currently leading the Computer Vision team at Oxy, I specialize in building scalable real-time and edge-deployed ML systems that enhance health, safety, and operational efficiency, driving measurable business impact. My expertise spans computer vision, deep learning, time series forecasting, predictive maintenance, anomaly detection, optimization, and physics-based modeling, with hands-on integration of multimodal vision-language models and foundation models. I lead cross-functional teams through the full product lifecycle, from ideation to deployment, and cultivate a culture of innovation. Passionate about leveraging cutting-edge AI to transform industrial operations and accelerate business success.Technical Skills: Artificial Intelligence, Computer Vision, Machine Learning, Deep Learning, Data Science, Data Analysis, Data Visualization, Algorithm and Workflow Design, Optimization, Physics-Based Modeling

Experience

1. Team Lead Ai & Computer Vision

   Oxy

   Jul 2025 — Present · Houston, TX, US

   Leading a cross-functional team of computer vision and software engineers to establish Oxy’s computer vision pillar, architecting and delivering fully automated deployment pipelines for diverse workloads, including real-time streaming camera feeds, scheduled batch processing, and on-demand inference jobs for various computer vision tasks.• Spearheading the development and deployment of advanced computer vision solutions across surface and downhole operations, with a strong focus on real-time health and safety applications. These include Personal Protective Equipment (PPE) compliance detection, fall detection, fire and smoke monitoring, and leak detection, all optimized for edge devices to ensure rapid response in critical environments. Additionally, driving initiatives in equipment condition monitoring (e.g, compressor Pressure Safety Valve status), rust and corrosion detection, accurate well location identification, and image analysis from downhole measurement tools. Leveraging and fine-tuning state-of-the-art models for image classification, object detection, tracking, and segmentation, including VGG, ResNet, EfficientNet, YOLO family, Fast R-CNN, Vision Transformers (ViT), and Segment Anything Model (SAM), to deliver robust, scalable solutions for industrial safety and operational efficiency.• Leveraging state-of-the-art Vision-Language Models (VLMs) such as DeepSeek Vision, LLaMA-based multimodal models, and Qwen-VL to integrate multimodal reasoning into computer vision workflows, enabling context-

Education

• Curtin University

  Master of Science - MS, Petroleum Engineering

  2007 — 2009

• Petroleum University of Technology

  Bachelor's degree, Petroleum Enginnering

  2002 — 2006

• The University of Tulsa

  Doctor of Philosophy (PhD), Petroleum Engineering

  2009 — 2013

Skills

• Petroleum Geology
• Petrel
• Well Control
• Hydraulics
• Petroleum
• Well Testing
• Applications of Computational Fluid Dynamic (Cfd) in Drilling,
• Gas Influx Modeling and Simulation
• Cfd
• Fluid Mechanics
• Applications of Wired Drill String Technology
• Drilling
• Computer Programming
• Drilling Fluids
• Multiphase Flow
• Engineering
• Directional Drilling
• Managed Pressure Drilling
• Programming
• Reservoir Engineering
• Matlab
• Completion
• Reservoir Simulation
• Under-Balance Drilling
• Numerical Analysis
• Transient Multi-Phase Flow
• Wellbore Strengthening
• Fortran
• Well Installation
• Gas
• Well Design
• Reservoir Management
• Characterization
• Petroleum Engineering
• Fluids
• Modeling