Architect

Job Summary

Architect role for an MNC requiring extensive experience in Azure Databricks and Azure Machine Learning within media and entertainment domains delivering scalable hybrid cloud data and AI solutions optimizing content workflows and enabling advanced analytics while collaborating with cross functional teams in a day shift hybrid work model supporting global stakeholders.

Responsibilities

• Design end to end data and AI architectures using Azure Databricks and Azure Machine Learning to support advanced analytics and predictive capabilities for media and entertainment content workflows
• Develop optimized data pipelines and reusable components in Azure Databricks to process large scale structured and unstructured media datasets with high reliability and performance
• Define reference architectures and patterns for ingestion processing storage and consumption of media data ensuring alignment with enterprise standards and compliance requirements
• Collaborate closely with product owners data engineers and data scientists to translate business needs into scalable technical solutions that enhance audience engagement and content monetization
• Guide teams in implementing robust MLOps practices on Azure Machine Learning covering model training deployment monitoring and lifecycle management for production grade models
• Establish data quality governance and observability practices ensuring consistent and trusted datasets that drive decision making across media programming advertising and digital operations
• Optimize Databricks clusters notebooks and jobs for cost efficiency reliability and performance ensuring responsible use of compute resources in the hybrid cloud environment
• Partner with security and compliance stakeholders to design secure architectures that protect media assets and customer data while meeting regulatory and contractual obligations
• Create detailed solution design documents technical roadmaps and architecture decision records that clearly explain trade offs constraints and expected business outcomes
• Collaborate with cross functional teams to integrate analytics outputs and machine learning models into content recommendation personalization and campaign optimization journeys
• Drive adoption of best practices in coding testing documentation and environment management across Azure Databricks and Azure Machine Learning implementations
• Provide technical guidance and mentoring to implementation teams helping resolve complex design issues and ensuring architectural consistency across initiatives
• Engage with business stakeholders to explain solution capabilities and value in clear nontechnical language highlighting measurable impact on audience experience and revenue growth
  
  

• Possess extensive experience in designing and delivering large scale data engineering solutions using Azure Databricks with focus on media and entertainment use cases such as content usage analytics and campaign reporting
• Demonstrate advanced proficiency in Azure Machine Learning including model pipelines feature engineering experiment tracking and deployment for tasks such as recommendation and churn prediction
• Bring strong background in media and entertainment domain processes such as content lifecycle advertising operations digital distribution and audience analytics translating domain needs into technical architectures
• Exhibit solid knowledge of Azure cloud services such as data storage integration identity and monitoring to design robust interoperable solutions in a hybrid work model
• Showcase proficiency in modern data engineering practices including distributed processing data modeling orchestration and performance tuning within cloud native ecosystems
• Display strong capability in stakeholder communication documentation and workshop facilitation to align technical decisions with business priorities and long term platform strategies
• Apply sound understanding of responsible AI practices data privacy and compliance constraints to ensure solutions respect consumer rights and protect sensitive media and audience data