MLOPS Engineer

Role: MLOPS Engineer Location: Pan India Experience: 6 to 15 Years Notice Period : Immediate to 90 days Mode of Interview : In-Person

Key words -Skillset

• AWS SageMaker, Azure ML Studio, GCP Vertex AI

• MLFlow, KubeFlow, AirFlow, Github Actions, AWS CodePipeline

• Deployment pipeline, Inference pipeline, Monitoring pipeline, Retraining pipeline

• Experiment Tracking
• MLOps Architecture
• REST API publishing

· 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).