You will create MBSE artifacts to define the architecture of an AI system. You will construct structured models that represent system components, behavior flows, and retraining cycles.

You will apply managed cloud services to deploy scalable machine learning training jobs. You will configure distributed workloads and managed infrastructure to support reliable model training.

You will evaluate system performance and cost metrics to recommend architectural changes. You will interpret utilization logs and monitoring dashboards to balance efficiency and scalability.

You will create end-to-end AI architectures that meet scalability, latency, and fault-tolerance requirements. You will define system boundaries and performance targets aligned to production constraints.

You will create detailed component diagrams and interface specifications to guide system implementation. You will translate architectural decisions into structured documentation that engineering teams can execute.

You will apply APIs, message queues, and serialization formats to integrate services into existing systems. You will design communication patterns that support reliability and performance in distributed environments.

You will evaluate the deployment and operational health of production systems. You will interpret monitoring signals and performance indicators to guide stabilization and rollout decisions.

You will analyze stakeholder requirements to select appropriate AI frameworks, services, or platforms. You will evaluate trade-offs between managed services and custom model development.

You will create solution architectures by combining third-party services and custom models. You will design integrated systems that balance accuracy, cost, performance, and deployment constraints.

In this project, you will design the architecture for a scalable AI platform that integrates multiple AI services into a cohesive system. Rather than focusing on model experimentation, this project focuses on the engineering and architectural decisions required to move AI capabilities into production environments. You will simulate the role of an AI systems architect working with a product and engineering team to design a platform that analyzes customer engagement data, predicts churn risk, and delivers actionable insights to internal tools and dashboards. The goal is to translate business and technical requirements into a complete system architecture that integrates data pipelines, AI services, APIs, and cloud infrastructure. Your design will define how different components interact, how requests flow through the system, and how the platform scales reliably as usage grows. You will specify service interfaces, message workflows, deployment architecture, and monitoring strategies that support maintainable and production-ready AI systems. The final deliverable is a portfolio-ready architecture package that demonstrates your ability to analyze requirements, design system components, integrate AI services, and evaluate deployment considerations such as scalability, reliability, and operational monitoring.