MLOps Engineer

Role Overview: As an MLOps Engineer at our company, you will be responsible for developing scalable applications and platforms in an agile environment. You should be a self-motivated individual with a passion for problem-solving and continuous learning. Key Responsibilities: - Project Management (50%): - Front Door: Requirements, Metadata collection, classification & security clearance - Data pipeline template development - Data pipeline Monitoring development & support (operations) - Design, develop, deploy, and maintain production-grade scalable data transformation, machine learning and deep learning code, pipelines - Manage data and model versioning, training, tuning, serving, experiment and evaluation tracking dashboards - Manage ETL and machine learning model lifecycle: develop, deploy, monitor, maintain, and update data and models in production - Build and maintain tools and infrastructure for data processing for AI/ML development initiatives Qualification Required: - Experience deploying machine learning models into production environment - Strong DevOps, Data Engineering, and ML background with Cloud platforms - Experience in containerization and orchestration (such as Docker, Kubernetes) - Experience with ML training/retraining, Model Registry, ML model performance measurement using ML Ops open source frameworks - Experience building/operating systems for data extraction, ingestion, and processing of large data sets - Experience with MLOps tools such as MLFlow and Kubeflow - Experience in Python scripting - Experience with CI/CD - Fluency in Python data tools e.g. Pandas, Dask, or Pyspark - Experience working on large scale, distributed systems - Python/Scala for data pipelines - Scala/Java/Python for micro-services and APIs - HDP, Oracle skills & Sql; Spark, Scala, Hive and Oozie DataOps (DevOps, CDC) Additional Details: Omit this section as there are no additional details mentioned in the job description. Role Overview: As an MLOps Engineer at our company, you will be responsible for developing scalable applications and platforms in an agile environment. You should be a self-motivated individual with a passion for problem-solving and continuous learning. Key Responsibilities: - Project Management (50%): - Front Door: Requirements, Metadata collection, classification & security clearance - Data pipeline template development - Data pipeline Monitoring development & support (operations) - Design, develop, deploy, and maintain production-grade scalable data transformation, machine learning and deep learning code, pipelines - Manage data and model versioning, training, tuning, serving, experiment and evaluation tracking dashboards - Manage ETL and machine learning model lifecycle: develop, deploy, monitor, maintain, and update data and models in production - Build and maintain tools and infrastructure for data processing for AI/ML development initiatives Qualification Required: - Experience deploying machine learning models into production environment - Strong DevOps, Data Engineering, and ML background with Cloud platforms - Experience in containerization and orchestration (such as Docker, Kubernetes) - Experience with ML training/retraining, Model Registry, ML model performance measurement using ML Ops open source frameworks - Experience building/operating systems for data extraction, ingestion, and processing of large data sets - Experience with MLOps tools such as MLFlow and Kubeflow - Experience in Python scripting - Experience with CI/CD - Fluency in Python data tools e.g. Pandas, Dask, or Pyspark - Experience working on large scale, distributed systems - Python/Scala for data pipelines - Scala/Java/Python for micro-services and APIs - HDP, Oracle skills & Sql; Spark, Scala, Hive and Oozie DataOps (DevOps, CDC) Additional Details: Omit this section as there are no additional details mentioned in the job description.