Key words -Skillset

• AWS SageMaker, Azure ML Studio, GCP Vertex AI
• PySpark, Azure Databricks
• MLFlow, KubeFlow, AirFlow, Github Actions, AWS CodePipeline
• Kubernetes, AKS, Terraform, Fast API

Responsibilities

• Model Deployment, Model Monitoring, Model Retraining
• Deployment pipeline, Inference pipeline, Monitoring pipeline, Retraining pipeline
• Drift Detection, Data Drift, Model Drift
• Experiment Tracking
• MLOps Architecture
• REST API publishing

Job Responsibilities:

· Research and implement MLOps tools, frameworks and platforms for our 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.

Required experience and qualifications:

· Wide experience with Kubernetes.

· Experience in operationalization of Data Science projects (MLOps) using at least one of the popular frameworks or platforms (e.g. Kubeflow, AWS Sagemaker, Google AI Platform, Azure Machine Learning, DataRobot, DKube).

· Good understanding of ML and AI concepts. Hands-on experience in ML model development.

· Proficiency in Python used both for ML and automation tasks. Good knowledge of Bash and Unix command line toolkit.

· Experience in CI/CD/CT pipelines implementation.

· Experience with cloud platforms - preferably AWS - would be an advantage.