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Position Overview:
As a Senior ML Ops Engineer at Kunato.AI, you will play a pivotal role in managing and productionizing machine learning models, ensuring their efficiency, performance, and reliability. You will be responsible for a wide range of tasks, from model versioning, writing scalable micro services, deploying services in cloud platform using Kubernetes.
Roles and Responsibilities:
Manage model training and automated retraining processes to keep models up to date.
Establish end-to-end model workflows, from onboarding and operations to decommissioning.
Establish ML Ops CI CD pipeline
Building Scalable Micro Services to move the ml models to production
Implement model version tracking and governance for better model management.
Oversee data archival and version management to maintain data integrity.
Monitor models and data drift to detect and respond to anomalies.
Provide best practices and execute proofs of concept (POCs) for automated and efficient model operations at scale.
Design and develop scalable MLOps frameworks tailored to client requirements.
Serve as the MLOps expert for the sales team, offering technical design solutions to support RFPs and client needs.
Skillset for the Role:
Strong Knowledge and Hands on Experience with Python frameworks (Fast Api, Flask, Pandas, NumPy)
Experience with any of the cloud platform (GCP, AWS, Azure, Digital Ocean)
Experience in Kubernetes, Docker or equivalent
Experience in any one of these Airflow, MLflow, Kubeflow
Basic Knowledge about ML algorithms and frameworks
Demonstrated ability to design and develop scalable MLOps frameworks.
Experience in SQL and NoSQL databases
As a Senior ML Ops Engineer at Kunato.AI, you will play a pivotal role in managing and productionizing machine learning models, ensuring their efficiency, performance, and reliability. You will be responsible for a wide range of tasks, from model versioning, writing scalable micro services, deploying services in cloud platform using Kubernetes.
Roles and Responsibilities:
Manage model training and automated retraining processes to keep models up to date.
Establish end-to-end model workflows, from onboarding and operations to decommissioning.
Establish ML Ops CI CD pipeline
Building Scalable Micro Services to move the ml models to production
Implement model version tracking and governance for better model management.
Oversee data archival and version management to maintain data integrity.
Monitor models and data drift to detect and respond to anomalies.
Provide best practices and execute proofs of concept (POCs) for automated and efficient model operations at scale.
Design and develop scalable MLOps frameworks tailored to client requirements.
Serve as the MLOps expert for the sales team, offering technical design solutions to support RFPs and client needs.
Skillset for the Role:
Strong Knowledge and Hands on Experience with Python frameworks (Fast Api, Flask, Pandas, NumPy)
Experience with any of the cloud platform (GCP, AWS, Azure, Digital Ocean)
Experience in Kubernetes, Docker or equivalent
Experience in any one of these Airflow, MLflow, Kubeflow
Basic Knowledge about ML algorithms and frameworks
Demonstrated ability to design and develop scalable MLOps frameworks.
Experience in SQL and NoSQL databases
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