Cynet systems Inc
Responsibilities
Design, build, and maintain scalable ETL/ELT pipelines using Azure Data Factory, Synapse Pipelines, and Databricks.
Ingest, transform, and curate structured and unstructured data from diverse sources for AI/ML consumption.
Optimize data workflows for performance, reliability, and cost-efficiency.
Enable data access and preparation for Azure AI services including Azure Machine Learning, Azure OpenAI, and Cognitive Services.
Collaborate with data scientists and ML engineers to operationalize AI models using MLOps and GenAIOps practices.
Support RAG (Retrieval-Augmented Generation) and other GenAI patterns through data engineering best practices.
Implement CI/CD pipelines for data workflows using Azure DevOps or GitHub Actions.
Automate infrastructure provisioning using Terraform, Bicep, or Client templates.
Monitor and troubleshoot data pipelines and cloud resources using Azure Monitor and Log Analytics.
Skills and Qualifications
8-10+ years of experience in data engineering, cloud platforms, and big data technologies.
Strong proficiency in Azure services: Data Factory, Synapse, Data Lake, Azure SQL, Cosmos DB.
Experience with Python, Spark, SQL, and distributed data processing.
Familiarity with AI/ML workflows and tools such as Azure ML, MLflow, and OpenAI APIs.
Understanding of data governance, security, and compliance in cloud environments.
#J-18808-Ljbffr
Design, build, and maintain scalable ETL/ELT pipelines using Azure Data Factory, Synapse Pipelines, and Databricks.
Ingest, transform, and curate structured and unstructured data from diverse sources for AI/ML consumption.
Optimize data workflows for performance, reliability, and cost-efficiency.
Enable data access and preparation for Azure AI services including Azure Machine Learning, Azure OpenAI, and Cognitive Services.
Collaborate with data scientists and ML engineers to operationalize AI models using MLOps and GenAIOps practices.
Support RAG (Retrieval-Augmented Generation) and other GenAI patterns through data engineering best practices.
Implement CI/CD pipelines for data workflows using Azure DevOps or GitHub Actions.
Automate infrastructure provisioning using Terraform, Bicep, or Client templates.
Monitor and troubleshoot data pipelines and cloud resources using Azure Monitor and Log Analytics.
Skills and Qualifications
8-10+ years of experience in data engineering, cloud platforms, and big data technologies.
Strong proficiency in Azure services: Data Factory, Synapse, Data Lake, Azure SQL, Cosmos DB.
Experience with Python, Spark, SQL, and distributed data processing.
Familiarity with AI/ML workflows and tools such as Azure ML, MLflow, and OpenAI APIs.
Understanding of data governance, security, and compliance in cloud environments.
#J-18808-Ljbffr