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Location

Bangalore | India

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Job description

MLOps Engineer

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.

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Salary