Senior LLMOps Engineer

Senior LLMOps Engineer

About CINC Systems

CINC Systems is the leading provider of accounting and management software for the community association management industry. Our platform supports tens of thousands of associations and millions of homes, operating in a highly regulated, data-sensitive, SaaS environment. We are building AI-native capabilities into the core of our platform, not as experiments, but as reliable, scalable systems that deliver real value to customers. The Senior LLMOps Engineer plays a critical role in making AI production-ready, observable, safe, and cost-effective.

About The Role

The Senior LLMOps Engineer is a hands-on technical leader responsible for operating, scaling, and governing large language model capabilities across the CINC platform. This role focuses on the systems and practices that sit between AI engineering and production operations: orchestration, evaluation, observability, safety, and cost control. This is a production engineering role for someone who understands that AI systems must be treated like any other critical software system, with strong fundamentals, clear feedback loops, and disciplined operations.

Key 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.

Qualifications

Technical Experience

• 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.

Leadership and Collaboration

• 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.

Mindset and Values

• 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.

What Success Looks Like

• LLM-powered features are reliable, observable, and cost-effective in production.
• Engineers can ship AI-enabled capabilities with confidence and clear guardrails.
• AI quality and performance issues are detected early and addressed quickly.
• The organization develops strong operational discipline around AI systems.
• The Senior LLMOps Engineer is recognized as a trusted expert and partner across engineering.

CINC is an Equal Opportunity Employer.Key skills/competency

• LLMOps
• Large Language Models
• Production Engineering
• Observability
• Cost Management
• Cloud Native Architectures
• Distributed Systems
• APIs
• DevOps
• AI Systems