Integration Architect - Pune, India - Arting Digital Private Limited

Description

• Machine Learning: Proficiency in Machine Learning and tools for model development, deployment, and management.
  
• Azure Data Factory and Databricks:
  
• Azure Datalake Storage:
  
• Pyspark
  

• Azure Kubernetes Service (AKS): Understanding of AKS for deploying and managing containerized AI models at scale.
  
• Azure DevOps
  
• Azure Cognitive Services: Knowledge of Azure Cognitive Services, such as Computer Vision, Natural
  
• Language Processing, or Speech Recognition, for integrating pre-built AI capabilities into applications.
  
• Azure Security: Knowledge of Azure security measures and best practices to ensure the protection of data and compliance with regulations.
  

• Integration Planning: The architect works closely with data engineers, data scientists, and other stakeholders to understand the requirements and design an integration plan. They consider factors like data sources, data formats, data quality, and system compatibility.
  
• Data Pre-processing: The architect is responsible for designing and implementing data pre-processing pipelines that prepare the input data for the AI model. This involves tasks like data cleaning, normalization, feature engineering, and data transformation.
  
• Model Integration: The architect collaborates with data scientists to integrate the AI model into the existing infrastructure. They ensure that the model can seamlessly consume the pre-processed data and provide accurate predictions or insights.
  
• Post-processing Pipelines: After the AI model generates results, the architect designs post-processing pipelines to transform and analyze the output. This may involve tasks like result interpretation, visualization, and further analysis.
  
• Quality Assurance: The architect focuses on ensuring the quality of the AI model and its results when
  exposed to actual data in production. They perform rigorous testing, validation, and monitoring to identify any issues or discrepancies. They also work on improving the models performance and addressing any potential biases or errors.
  
• Scalability and Performance: The architect considers scalability and performance aspects while integrating the AI model. They optimize the pipelines to handle large volumes of data efficiently and ensure that the system can handle real-time or near-real-time processing requirements.
  
• Documentation and Collaboration: The architect maintains documentation of the integration process, including technical specifications, workflows, and any customizations made. They collaborate with cross-functional teams to address any challenges or requirements during the integration process.