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Lead MLOps Engineer - Real-Time AI & Video Solutions (Hybrid) Office Location: San Jose (Hybrid) Job Type: Full-time Join an innovative AI company that is at the forefront of real-time AI and video applications. Our team boasts leading experts in computer graphics and generative modeling, pioneering the future of media. We are seeking experienced MLOps Engineers eager to contribute to groundbreaking real-time AI applications. The Role As a MLOps Engineer , you will be instrumental in building and maintaining cutting-edge machine learning pipelines and infrastructure. Collaborating closely with AI researchers, data scientists, and software engineers, you will deploy advanced models into production, optimize real-time inference, and ensure our systems scale efficiently. What You'll Do Design and enhance ML pipelines for training, validation, and inference processes. Automate the deployment of deep learning and generative models for real-time applications. Implement versioning strategies, ensure reproducibility, and establish rollback capabilities. Deploy and maintain containerized ML solutions across cloud platforms like AWS, GCP, and Azure. Optimize model performance utilizing TensorRT, ONNX Runtime, and PyTorch. Leverage GPUs, distributed computing, and parallel processing to enhance AI workloads. Establish and maintain CI/CD pipelines using tools such as GitHub Actions, Jenkins, or ArgoCD. Automate model retraining, monitoring, and performance evaluation processes. Ensure adherence to privacy, security, and AI ethics standards. What You Bring 3+ years of experience in MLOps, DevOps, or AI model deployment. Proficient in Python and familiar with frameworks like TensorFlow, PyTorch, and ONNX. Experience with Docker, Kubernetes, and serverless architecture. Hands-on knowledge of ML tools including ArgoWorkflow, Kubeflow, MLflow, and Airflow. Experienced in deploying and optimizing GPU-based inference applications (CUDA, TensorRT, DeepStream). Strong understanding of CI/CD practices and scalable ML infrastructure. A passion for automation and clean, maintainable system design. A solid grasp of distributed systems. A Bachelor's or Master's degree in Computer Science or equivalent work experience. Bonus Skills Experience with CUDA programming. Exposure to LLMs and generative AI in production environments. Familiarity with distributed computing frameworks (Ray, Horovod, Spark). Experience with Edge AI deployment (Triton Inference Server, TFLite, CoreML). Basic networking knowledge. Please apply now for more details and the next steps. We look forward to hearing from you!