This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role focuses on designing, implementing, and operationalizing AI systems with a strong emphasis on structured evaluation, measurable quality, and production-grade reliability. - 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 evaluations 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 structured and unstructured enterprise data. - Develop and manage fine-tuning workflows (SFT, preference optimization, or related techniques) including dataset preparation, versioning, and validation. Qualifications - 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#, Java, or a similar language 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. Requirements - 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). Company 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.