Should have minimum of 10 years of IT experience Working experience in Machine learning

Operate and maintain systems supporting the provisioning of new clients, applications, and features.

Day-to-day monitoring of the Production service delivery environment to ensure all services and applications are operating optimally and SLAs are met.

Software deployment and configuration management in both QA and Production environments.

Collaborate with Data Scientists and Data Engineers on feature development teams to containerize and build out deployment pipelines for new modules

Design, build and optimize applications' containerization and orchestration with Docker and Kubernetes and AWS or Azure Automate applications and infrastructure deployments

Produce build and deployment automation scripts to integrate between services

Be a subject matter expert on DevOps practices, CI/CD and Configuration Management with assigned engineering team

Experience with cloud computing platforms: Google Cloud, Amazon Web Service, Azure, and Kubernetes.

Experience in Azure cloud services (especially Azure Devops) and deploying production code in the same Experience in MLFlow, Qubeflow, MLTracking, ML Experiments

Experience in big data technologies preferred: Hadoop, Hive, Spark, and Kafka.

Knowledge of machine learning frameworks: Tensor flow, Caffe/Caffe2, Pytorch, Keras, MXNet, Scikit-Learn.Collects data, analyzes and reports for early detection and correction, continual improvement.

Recognize and attend to important details with accuracy and efficiency.