微软Software Engineer - Azure Real-time Messaging Services

• Architects and builds scalable core services, including APIs and microservices for publisher onboarding, content catalog management, entitlement, and billing, ensuring secure integration with LLM clients for seamless content consumption. • Designs and implements robust testing and analytics systems, developing ingestion pipelines for premium content, enabling real-time usage tracking, billing insights, and performance monitoring, while leveraging AI tools for test automation. • Leads architecture and infrastructure design, creating systems that support subscription models, pay-per-use billing, and licensing compliance, while optimizing backend performance for high availability and low latency in AI-driven environments. • Drives engineering excellence, applying secure coding practices, automation in deployment, and compliance validation, while continuously improving code quality, maintainability, and performance through metrics and best practices. • Guides implementation and release planning, breaking down work into manageable tasks, estimating timelines, and ensuring disaster recovery, rollback strategies, and secure deployment processes are in place. • Champions reliability and supportability, integrating telemetry and monitoring for live site operations, conducting incident retrospectives, and improving observability to reduce downtime and enhance system resilience. • Leads with mentorship and user-centric design, guiding technical decisions, mentoring junior engineers, and collaborating with stakeholders to incorporate customer insights, security, and privacy into solution design and delivery. 

• Architects and builds scalable core services, including APIs and microservices for publisher onboarding, content catalog management, entitlement, and billing, ensuring secure integration with LLM clients for seamless content consumption.  • Designs and implements robust testing and analytics systems, developing ingestion pipelines for premium content, enabling real-time usage tracking, billing insights, and performance monitoring, while leveraging AI tools for test automation.  • Leads architecture and infrastructure design, creating systems that support subscription models, pay-per-use billing, and licensing compliance, while optimizing backend performance for high availability and low latency in AI-driven environments.  • Drives engineering excellence, applying secure coding practices, automation in deployment, and compliance validation, while continuously improving code quality, maintainability, and performance through metrics and best practices.  • Guides implementation and release planning, breaking down work into manageable tasks, estimating timelines, and ensuring disaster recovery, rollback strategies, and secure deployment processes are in place.  • Champions reliability and supportability, integrating telemetry and monitoring for live site operations, conducting incident retrospectives, and improving observability to reduce downtime and enhance system resilience. 

• Build, maintain, and enhance data ETL pipelines for processing large-scale data with low latency and high throughput to support Copilot operations.• Own data quality initiatives including monitoring, validation, and remediation to ensure integrity across attribution datasets and downstream dashboards.• Implement schema management solutions that enable quick and seamless evolution of attribution data without disrupting consumers.• Develop and maintain infrastructure that supports both batch and real-time attribution requirements.• Collaborate with product managers, marketing analysts, and data scientists to deliver insights for campaign optimization and growth strategies.• Design scalable attribution data architectures that can handle growing data volumes and evolving business needs.• Implement comprehensive monitoring and observability solutions for attribution pipelines, including SLA tracking and automated alerting.

• In this role, you will • - AI Solution Development: Design, develop, and deploy AI-driven applications, across domains, such as machine learning, NLP, and computer vision, addressing both business requirements and end-user needs. • - Agent Design and Development: Design and implement intelligent agents within multi-agent systems, enabling real-time collaboration based on pre-defined goals, strategies, and data exchanges. Develop agent-based models to optimize decision-making and interactions. • - MCP Integration: Extend and integrate Multi-Agent Coordination Platforms(MCP) to optimize resource allocation, communication, and decision-making across multiple agents in shared environments. • - Collaboration with Data Science Teams: Collaborate with data scientists to refine algorithms, optimize models, and enhance AI performance, focusing on model tuning, feature selection, and performance benchmarking • - Testing and Validation: Conduct rigorous testing and validation of AI models, including unit testing, integration testing, and A/B testing, to ensure accuracy, reliability, and scalability before deployment. • - Monitoring and Maintenance: Monitor deployed AI models, track performance metrics, and implement continuous improvement strategies, including model re-training and updates based on real-world data and evolving business needs.