Computer Vision, MLOps, Python

Job Description Computer Vision & MLOps Engineer Experience: 2-20 Years Job Summary We are seeking talented Computer Vision & MLOps Engineers with strong expertise in Computer Vision, Machine Learning Operations (MLOps), and Python. The ideal candidate will be responsible for developing, deploying, and maintaining AI/ML solutions for image and video analytics while building scalable MLOps pipelines for model training, deployment, monitoring, and lifecycle management. Key Responsibilities Design, develop, and deploy Computer Vision and Deep Learning solutions. Build and optimize image and video processing pipelines for real-world applications. Develop and maintain scalable MLOps platforms for model training, deployment, monitoring, and retraining. Implement model versioning, experiment tracking, and CI/CD pipelines for ML solutions. Work with structured and unstructured datasets for model development and optimization. Deploy AI/ML solutions on cloud and edge environments. Monitor model performance, drift detection, and production health metrics. Collaborate with Data Scientists, Software Engineers, Product Teams, and Business Stakeholders. Optimize model accuracy, inference latency, and resource utilization. Develop APIs and services to integrate AI models into enterprise applications. Ensure compliance with security, governance, and best practices for AI deployment. Job Description Computer Vision & MLOps Engineer Experience: 2-20 Years Job Summary We are seeking talented Computer Vision & MLOps Engineers with strong expertise in Computer Vision, Machine Learning Operations (MLOps), and Python. The ideal candidate will be responsible for developing, deploying, and maintaining AI/ML solutions for image and video analytics while building scalable MLOps pipelines for model training, deployment, monitoring, and lifecycle management. Key Responsibilities Design, develop, and deploy Computer Vision and Deep Learning solutions. Build and optimize image and video processing pipelines for real-world applications. Develop and maintain scalable MLOps platforms for model training, deployment, monitoring, and retraining. Implement model versioning, experiment tracking, and CI/CD pipelines for ML solutions. Work with structured and unstructured datasets for model development and optimization. Deploy AI/ML solutions on cloud and edge environments. Monitor model performance, drift detection, and production health metrics. Collaborate with Data Scientists, Software Engineers, Product Teams, and Business Stakeholders. Optimize model accuracy, inference latency, and resource utilization. Develop APIs and services to integrate AI models into enterprise applications. Ensure compliance with security, governance, and best practices for AI deployment.