Principal AI Engineer

Risepoint

About The Position

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.

Requirements

8+ years of software engineering experience with demonstrated progression into architecture ownership including hands-on experience with Kubernetes (AKS preferred), containerization (Docker), and distributed system design at production scale.
A track record of setting technical direction across engineering teams, not just executing within one: including defining standards others build against and influencing architectural decisions in cross-functional environments.
Deep experience with autoscaling policy design, resource governance, and cost management in cloud environments (Azure preferred; AWS or GCP acceptable), managed through infrastructure as code.
Experience translating business and product requirements into infrastructure architecture including capacity planning, SLA definition, and trade-off communication to non-technical stakeholders.
Proficiency in Python, C#, Java, or a comparable language used in production systems, with strong fundamentals in object-oriented programming and design patterns.

Nice To Haves

Architecture or deployment experience with AI/ML systems in cloud environments.
Managed integrations with Databricks model serving endpoints and vector stores a plus.
Implementation experience with Azure AI Foundry and realtime models.
Experience designing APIs and backend systems supporting high concurrency and real-time interactions.
Familiarity with RAG systems, vector stores, and MCP server architecture.

Responsibilities

Lead the architecture and evolution of cloud-native infrastructure for the Student Journey Platform, including all services and its integrated platforms (Salesforce, DBX, Marketing sites, Azure AI Foundry), setting technical direction across Kubernetes-based AI services deployed on Azure (AKS), with accountability for platform-wide scalability, reliability, and cost efficiency
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.
Lead architecture design and implementation across the platform’s components for SJP, Student Success Team, Marketing Technology, and Enterprise Data Platform implementing a variety of resources (Kafka, Azure Event Hubs, Azure Service Bus, AI Foundry), ensuring asynchronous AI workloads are resilient, observable, and operate without bottlenecks, vulnerabilities, or data loss under production conditions.
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.
Present sound architecture proposals to the Architecture Review Board (ARB) for approval of products intended for release into production, representing the Student Journey Platform’s technical strategy as well as its integrated products and services, ensuring alignment with Risepoint’s enterprise standards for SLAs, security, compliance, and scalability.
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.