SKE Risepoint is hiring a Senior AI Engineer to design, implement, and operationalize AI systems with a concentration on structured evaluation, measurable quality, and production-grade reliability. This role contributes directly to an AI-powered Student Journey Platform central to the organization's long-term strategy and its mission to increase access to affordable education.

What You'll Do

Build and maintain evaluation frameworks (LLM-as-Judge, rubric-based scoring, regression test suites) to measure output quality, reliability, and drift.
Architect and implement multi-agent workflows with clear coordination, tool usage, and failure handling patterns.
Build structured observability into AI systems (tracing, prompt/version tracking, evaluation logging, cost and latency monitoring).
Define and enforce quality gates for AI features using automated evals prior to production release.
Optimize inference performance (latency, token usage, caching, batching, routing across models).
Collaborate with product and engineering teams to translate business requirements into testable AI system designs.
Contribute to code reviews, architectural discussions, and internal standards for AI development.
Design and implement Retrieval-Augmented Generation (RAG) systems and Model Context Protocol (MCP) servers using enterprise data.
Develop and manage fine-tuning workflows (SFT, preference optimization) including dataset preparation, versioning, and validation.

What We're Looking For

3-5 years of full stack engineering experience with strong fundamentals in object-oriented programming, applicable design patterns, and AI-focused system design.
Professional experience in Python, C#, or Java used in production systems.
Experience with LLM evaluation and observability tooling (e.g. Langfuse, LangSmith, OpenTelemetry-based tracing, custom evaluation harnesses).
Experience implementing guardrails, policy enforcement, and safety layers in AI driven systems while leveraging LLM-as-Judge for validation and continuous improvement.

Nice to Have

Familiarity with performance optimization techniques for LLM-based systems (latency, caching, routing, batching).
Experience building production-grade RAG systems (retrieval pipelines, chunking strategies, embeddings, reranking, context construction).
Experience contributing to internal AI standards, reusable frameworks, or platform-level tooling.
Experience deploying AI systems in cloud environments (AWS, Azure, GCP).
Experience in Databricks (model serving endpoints, ML Flow).