MLOPS Engineer

As an MLOps Engineer, your role will involve researching and implementing MLOps tools, frameworks, and platforms for Data Science projects. You will work on activities to enhance MLOps maturity in the organization and introduce a modern, agile, and automated approach to Data Science. Conducting internal training and presentations on the benefits and usage of MLOps tools will also be part of your responsibilities. Key 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 Qualifications Required: - Wide experience with Kubernetes - Experience in operationalization of Data Science projects (MLOps) using at least one popular framework or platform (e.g. Kubeflow, AWS SageMaker, Google AI Platform, Azure Machine Learning, DataRobot, DKube) - Good understanding of ML and AI concepts with hands-on experience in ML model development - Proficiency in Python for both ML and automation tasks, along with knowledge of Bash and Unix command line - Experience in CI/CD/CT pipelines implementation - Experience with cloud platforms, preferably AWS, would be an advantage Additionally, you should have expertise in the following skillset: - AWS SageMaker, Azure ML Studio, GCP Vertex AI - PySpark, Azure Databricks - MLFlow, KubeFlow, AirFlow, Github Actions, AWS CodePipeline - Kubernetes, AKS, Terraform, Fast API This role provides an opportunity to contribute to the enhancement of MLOps processes within the organization and work with cutting-edge technologies in the field of Data Science and Machine Learning Operations. As an MLOps Engineer, your role will involve researching and implementing MLOps tools, frameworks, and platforms for Data Science projects. You will work on activities to enhance MLOps maturity in the organization and introduce a modern, agile, and automated approach to Data Science. Conducting internal training and presentations on the benefits and usage of MLOps tools will also be part of your responsibilities. Key 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 Qualifications Required: - Wide experience with Kubernetes - Experience in operationalization of Data Science projects (MLOps) using at least one popular framework or platform (e.g. Kubeflow, AWS SageMaker, Google AI Platform, Azure Machine Learning, DataRobot, DKube) - Good understanding of ML and AI concepts with hands-on experience in ML model development - Proficiency in Python for both ML and automation tasks, along with knowledge of Bash and Unix command line - Experience in CI/CD/CT pipelines implementation - Experience with cloud platforms, preferably AWS, would be an advantage Additionally, you should have expertise in the following skillset: - AWS SageMaker, Azure ML Studio, GCP Vertex AI - PySpark, Azure Databricks - MLFlow, KubeFlow, AirFlow, Github Actions, AWS CodePipeline - Kubernetes, AKS, Terraform, Fast API This role provides an opportunity to contribute to the enhancement of MLOps processes within the organization and work with cutting-edge technologies in the field of Data Science and Machine Learning Operations.