Skill

ML Model Deployment

Data Science, Analytics and AI/ML

ML model deployment is the process of taking a trained machine learning model and making it available for use in a production environment, such as via an API, batch job, or embedded application. It involves packaging the model, managing dependencies, ensuring scalability and low latency, and integrating it into existing software systems. ML engineers and MLOps practitioners handle deployment using tools like Docker, Kubernetes, and cloud services such as AWS SageMaker or Vertex AI.

Open roles requiring ML Model Deployment (1)

Senior AI Infrastructure Engineer - EDA Infrastructure

NVIDIA

$184,000 – $356,500

NVIDIA is seeking an infrastructure engineer to build scalable telemetry, observability, and inventory management systems that support their GPU cloud services and AI platforms. The role bridges product, infrastructure, and software engineering to create automated, intelligence-driven systems that improve operational visibility and reduce manual toil across NVIDIA's hardware and software infrastructure.

Listed on NVIDIA’s careers site · Apply there ↗

Roles that use ML Model Deployment

• Infrastructure Engineer1

• CI/CD
• Kubernetes
• Docker
• MLOps
• AWS SageMaker