MLOps Engineer

NTT DATA is a global innovator of business and technology services, and they are seeking an MLOps Engineer to develop ML applications across various domains. The role involves utilizing Azure ML Studio and Kubernetes, deploying cloud services, and ensuring model validation and quality for client satisfaction.

Responsibilities:

• Exercise expertise in ideating and developing ML applications on prediction, recommendation, text analytics, computer vision, bots, and document intelligence
• Demonstrate deep knowledge of ML frameworks such as TensorFlow, PyTorch, Keras, Spacy, and scikit-learn
• Employ technical knowledge and hands-on experience with Azure ML Studio and Azure Kubernetes Service
• Experience in deploying Azure cloud services using Terraform templates with strong knowledge of DevOps principles and automated deployments
• Leverage advanced knowledge of Python open-source software stack such as Django or Flask, Django Rest or FastAPI, etc
• Work on model inferencing, validation and deployments to ensure models are deployed with the appropriate levels of validation and quality
• Create and maintain infrastructure to ingest, normalize, and combine datasets for actionable insights
• Interact at appropriate levels to ensure client satisfaction and project success
• Communicate complex technical concepts clearly to non-technical audiences

Requirements:

• 8+ Years experience with: MLOps Azure Kubernetes Argo / Bento Azure ML Studio Model Inferencing
• Exercise expertise in ideating and developing ML applications on prediction, recommendation, text analytics, computer vision, bots, and document intelligence
• Demonstrate deep knowledge of ML frameworks such as TensorFlow, PyTorch, Keras, Spacy, and scikit-learn
• Employ technical knowledge and hands-on experience with Azure ML Studio and Azure Kubernetes Service
• Experience in deploying Azure cloud services using Terraform templates with strong knowledge of DevOps principles and automated deployments
• Leverage advanced knowledge of Python open-source software stack such as Django or Flask, Django Rest or FastAPI, etc
• Work on model inferencing, validation and deployments to ensure models are deployed with the appropriate levels of validation and quality
• Create and maintain infrastructure to ingest, normalize, and combine datasets for actionable insights
• Interact at appropriate levels to ensure client satisfaction and project success
• Communicate complex technical concepts clearly to non-technical audiences