Risepoint→
Senior AI Engineer (Cloud Architecture… at Risepoint · Remote
Skills
Job Description
Risepoint is an education technology company that provides world-class support and trusted expertise to more than 100 universities and colleges. We primarily work with regional universities, helping them develop and grow their high-ROI, workforce-focused online degree programs in critical areas such as nursing, teaching, business, and public service. Risepoint is dedicated to increasing access to affordable education so that more students, especially working adults, can improve their careers and meet employer and community needs.
The Impact You Will Make
Risepoint is developing an AI-powered Student Journey Platform and is seeking a Senior AI Engineer with deep experience building and operating AI systems at enterprise scale. This role focuses on designing, deploying, and scaling production-grade AI services in cloud environments, with particular emphasis on distributed systems, event-driven architecture, orchestration layers, and Kubernetes-based workloads. The ideal candidate has experience integrating AI capabilities into resilient, high-availability systems that support real-world enterprise traffic and operational demands. This role contributes directly to a platform that is central to the organization’s long-term strategy.
How You Will Bring Our Mission to Life
What You Will Do
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.
What Success Looks Like
AI services scale predictably under increasing load, meeting defined SLAs for availability, latency, and throughput.
Event-driven pipelines process workloads reliably without bottlenecks or data loss, with appropriate retry and failure-handling mechanisms.
Kubernetes-based deployments are stable, observable, and horizontally scalable, supporting resilient operation under production conditions.
How Impact Will be Measured
AI services meet defined SLAs/SLOs for uptime, latency, and throughput, as measured through production monitoring tools (e.g., New Relic, Azure Monitor, Prometheus/Grafana) and alerting frameworks.
Event-driven and queue-based systems maintain consistent throughput and processing times under projected and stress-tested load, supporting business adoption and traffic growth targets without degradation or data loss.
Kubernetes-based workloads demonstrate effective horizontal scaling and resource utilization (CPU, memory, autoscaling policies), with cloud spend aligned to performance targets and capacity forecasts.
What You’ll Bring to the Team
Experience That Matters Most
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 That’s Great to Have
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)
Risepoint is an equal-opportunity employer and supports a diverse and inclusive workforce.