MLOPS Engineer
Role – MLOPS
Location \: Pan Indida
Experience \: 5+ to 12 Years
Mode of Interview - In Person (Chennai)
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.