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.