Senior AI Engineer (Cloud Architecture Concentration)

Risepoint is an education technology company that provides world-class support and trusted expertise to more than 100 universities and colleges. The Senior AI Engineer will focus on designing, deploying, and scaling production-grade AI services in cloud environments, contributing directly to the development of an AI-powered Student Journey Platform.

Responsibilities:

• Design and implement scalable AI service architectures in cloud environments (Azure preferred; AWS or GCP acceptable)
• Build event-driven systems using queues and messaging platforms (e.g., Azure Service Bus, RabbitMQ, SQS) to support asynchronous AI workloads
• Implement event streaming and real-time processing pipelines (e.g., Kafka, Azure Event Hubs, Pub/Sub, Kinesis)
• Architect, maintain, and scale containerized AI services deployed to Kubernetes, with emphasis on Azure Kubernetes Service (AKS)
• Design orchestration layers that manage model calls, downstream services, retries, rate limits, and failure handling
• Optimize system performance under load, including horizontal scaling, autoscaling policies, resource management, and cost control
• Implement WebSocket or real-time client communication patterns for interactive AI applications
• Contribute to infrastructure-as-code and CI/CD practices for AI service deployment, collaborating with CloudOps, DevOps, and application engineering teams to ensure reliability, availability, and operational standards are met
• Partner with Product and business stakeholders to translate projected traffic, adoption, and growth targets into scalable technical architectures and capacity plans and debug production level issues as needed

Requirements:

• 3-5 years of software engineering experience with strong fundamentals in object-oriented programming, design patterns, and distributed system design
• Professional experience in Python, C#, Java, or a similar language used in production systems
• Strong hands-on experience with containerization (Docker) and Kubernetes-based orchestration (AKS preferred)
• Experience integrating AI/LLM workloads into enterprise-grade distributed systems
• Experience designing APIs and backend systems that support high concurrency and real-time interactions
• Experience designing event-driven architectures using messaging systems (Azure Service Bus, RabbitMQ, SQS)
• Experience implementing event streaming systems (Kafka, Azure Event Hubs, Pub/Sub, Kinesis)
• Experience deploying AI systems in cloud environments (AWS, Azure, GCP)
• Experience in Databricks (model serving endpoints, ML Flow)