Description

Key Skills: AWS SageMaker, Azure ML Studio, GCP Vertex AI, PySpark, Azure Databricks, MLFlow, KubeFlow, AirFlow, Kubernetes, Terraform

Good to Have Skills: Github Actions, AWS CodePipeline, AKS, Fast API, Python programming, Bash scripting, Unix command line toolkit, CI/CD/CT pipelines, AWS cloud platform experience, DataRobot, DKube platforms

Roles & Responsibilities:

• Research and implement MLOps tools, frameworks and platforms for Data Science projects
• Work on a backlog of activities to raise MLOps maturity in the organization
• Proactively introduce a modern, agile and automated approach to Data Science
• Conduct internal training and presentations about MLOps tools' benefits and usage
• Handle model deployment, model monitoring, and model retraining processes effectively
• Manage deployment pipeline, inference pipeline, monitoring pipeline, and retraining pipeline
• Implement drift detection including data drift and model drift monitoring
• Maintain experiment tracking and MLOps architecture for optimal performance
• Develop and publish REST APIs for machine learning model integration

Experience Required: 5 to 14 years of experience with wide experience in Kubernetes, operationalization of Data Science projects using popular MLOps frameworks, good understanding of ML and AI concepts with hands-on ML model development experience, and proficiency in Python for ML and automation tasks

Key Skills

Education

Any Graduate