Senior LLMOps Engineer operating reliable, observable, and cost-effective AI systems. Productionizing LLM capabilities for CINC’s community association management software platform.
Responsibilities
Design and operate LLM orchestration and runtime systems supporting reliable, low-latency AI workflows
Build and maintain evaluation pipelines measuring quality, regressions, and business impact of LLM-driven features
Implement observability for AI systems, including tracing, metrics, and feedback loops at prompt, agent, and workflow levels
Establish LLM cost-management strategies including budgeting, rate limiting, caching, and optimization
Partner with AI and product engineers to productionize AI features safely and incrementally
Define and enforce security, privacy, and data-handling guardrails 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 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
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