Job Description
Role Overview:
Support the deployment, scaling, optimization, and monitoring of AI/ML models in production environments. Work closely with data scientists and developers to ensure models run efficiently, reliably, and with fast inference performance.
Key Responsibilities
Develop, maintain, and deploy ML/AI models into production environments.
Build and serve model inference APIs using frameworks like FastAPI.
Optimize models for better inference performance including quantization and model compression.
Package and containerize models using Docker and manage deployments with orchestration tools (e.g., Kubernetes).
Set up CI/CD pipelines and automation workflows for model deployment.
Monitor model performance, latency, and reliability in production.
Troubleshoot and resolve deployment, infrastructure, or inference issues.
Collaborate with ML Engineers, Data Scientists, and DevOps teams to streamline workflows.
Skills & Requirements
Proficiency...
Support the deployment, scaling, optimization, and monitoring of AI/ML models in production environments. Work closely with data scientists and developers to ensure models run efficiently, reliably, and with fast inference performance.
Key Responsibilities
Develop, maintain, and deploy ML/AI models into production environments.
Build and serve model inference APIs using frameworks like FastAPI.
Optimize models for better inference performance including quantization and model compression.
Package and containerize models using Docker and manage deployments with orchestration tools (e.g., Kubernetes).
Set up CI/CD pipelines and automation workflows for model deployment.
Monitor model performance, latency, and reliability in production.
Troubleshoot and resolve deployment, infrastructure, or inference issues.
Collaborate with ML Engineers, Data Scientists, and DevOps teams to streamline workflows.
Skills & Requirements
Proficiency...
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