Senior LLMOps Engineer

CINC Systems is the leading provider of accounting and management software for the community association management industry. The Senior LLMOps Engineer plays a critical role in making AI production-ready, observable, safe, and cost-effective by operating, scaling, and governing large language model capabilities across the CINC platform.

Responsibilities:

• Design and operate LLM orchestration and runtime systems that support reliable, low-latency AI workflows
• Build and maintain evaluation pipelines to measure quality, regressions, and business impact of LLM-driven features
• Implement observability for AI systems, including tracing, metrics, and feedback loops at the prompt, agent, and workflow levels
• Establish cost management strategies for LLM usage, including budgeting, rate limiting, caching, and optimization
• Partner with AI and product engineers to productionize AI features safely and incrementally
• Define and enforce guardrails for security, privacy, and data handling in AI workflows
• Support experimentation with new models and tools while ensuring production stability
• Improve incident readiness and response for AI-related failures and degradations
• Influence build versus buy decisions for LLM tooling and platforms
• Mentor engineers and help establish best practices for operating AI systems at scale

Requirements:

• 8+ years of experience in software engineering, platform engineering, or DevOps roles
• Hands-on experience operating LLM-powered systems in production
• Familiarity with LLM providers and orchestration frameworks
• Strong understanding of distributed systems, APIs, and cloud-native architectures
• Experience designing observability and evaluation systems for complex workflows
• Practical knowledge of cost and performance optimization in cloud environments
• Experience working with event-driven architectures and asynchronous workflows
• Proven ability to lead through influence rather than authority
• Highly structured thinker with strong problem-solving skills
• Clear communicator capable of explaining AI operational trade-offs to technical and non-technical stakeholders
• Comfortable working across teams in a fast-moving, evolving environment
• Builder mindset with a focus on reliability and outcomes
• Belief that AI amplifies engineering fundamentals rather than replaces them
• Learning-first attitude, staying current with evolving AI tools and practices
• Pragmatic and calm under pressure, especially during incidents
• Customer-aware, understanding the real-world impact of AI behavior