Data Scientist

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
• 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.