Computer Vision, MLOps, Python (Bengaluru)

Computer Vision & MLOps Engineer Experience: 2-20 Years Job Summary We are seeking talented Computer Vision & MLOps Engineers with solid 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. Computer Vision & MLOps Engineer Experience: 2-20 Years Job Summary We are seeking talented Computer Vision & MLOps Engineers with solid 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.