MLOPS Engineer
Summary
The MLOps Engineer will design and implement automated pipelines for model deployment, monitoring, and retraining within Data Science projects. The role requires expertise in cloud-based ML platforms, CI/CD/CT practices, and container orchestration using tools like Kubernetes, AWS SageMaker, and Kubeflow.
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.