Principal AI Engineer

Responsibilities

• Establish and promote architecture standards within the platform scope, design patterns, and deployment best practices that all engineering teams build against including service mesh configuration, autoscaling policy design, resource governance, API contracts, and container orchestration strategy.
• Align platform architecture to business growth needs and scalability requirements, partnering with Product, Engineering, and business stakeholders to ensure infrastructure decisions stay ahead of adoption curves not reactive to them.
• Identify and resolve architectural risk early, before it compounds, working across engineering teams to close gaps in design, security posture, or operational readiness.
• Debug and resolve production-level issues where infrastructure or architecture is a contributing factor, driving root cause resolution rather than symptomatic fixes.
• Implement and manage event streaming and real-time processing pipelines (e.g., Kafka, Azure Event Hubs, Pub/Sub, Kinesis) at production scale, supporting high-volume asynchronous AI workloads.
• Design and manage multi-tenant cloud infrastructure across university partner deployments, each with potentially distinct compliance, data isolation, and availability requirements.
• What Success Looks Like
• Engineering teams across the Student Journey Platform build with confidence against clear, documented architecture standards without requiring repeated escalation or one-off guidance on foundational decisions.
• Kubernetes-based deployments are stable, observable, and horizontally scalable, supporting resilient operation under production load with well-defined SLAs for availability, latency, and throughput.
• Infrastructure decisions made today hold up 12-18 months from now, as the platform scales across additional university partners and AI-mediated workloads.
• How Impact Will be Measured
• Kubernetes workloads demonstrate effective horizontal scaling and resource utilization, with cloud spend aligned to performance targets and capacity forecasts.
• Event-driven and queue-based systems maintain consistent throughput and processing times under load, supporting business adoption targets wi

Benefits

Additional Information

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 building our Student Journey Platform, a multi-component AI platform spanning real-time orchestration, machine learning model serving, event-driven workflow execution, and a student intelligence layer operating across 10+ university partner environments. The role will lead the technical direction for platform architecture within the engineering team: a Cadence Engine supporting durable stateful student engagement workflows; real-time AI-mediated communication endpoints across web chat, phone AI, and SMS AI with sub-second latency requirements; ML model serving infrastructure for propensity scoring and lead prioritization at scale; multi-tenant Kubernetes cluster architecture across partner deployments with distinct compliance and data isolation requirements; and a speech analytics pipeline processing call transcript data at volume. This is an architect-level role in scope and accountability charged with setting technical direction, defining the standards engineering teams build against, and keeping the platform ahead of Risepoint's growth curve. How You Will Bring Our Mission to Life

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