Senior LLMOps Engineer

Senior LLMOps Engineer

At CINC Systems, we're building AI-native capabilities directly into the core of our platform, aiming for reliable, scalable systems that provide tangible value to our customers. As a Senior LLMOps Engineer, you'll be instrumental in making AI production-ready, observable, secure, and cost-effective within our highly regulated, data-sensitive, multi-tenant SaaS environment.

CINC Systems is the leading provider of accounting and management software for the community association management industry, supporting tens of thousands of associations and millions of homes.

About The Role

This hands-on technical leadership role focuses on operating, scaling, and governing large language model capabilities across the CINC platform. It emphasizes the systems and practices that bridge AI engineering and production operations, including orchestration, evaluation, observability, safety, and cost control. This is a production engineering role for individuals who understand that AI systems are critical software and require strong fundamentals, clear feedback loops, and disciplined operations.

Key Responsibilities

• Design and operate LLM orchestration and runtime systems to support reliable, low-latency AI workflows.
• Build and maintain evaluation pipelines to measure quality, regressions, and business impact of LLM-driven features.
• Implement comprehensive observability for AI systems, covering tracing, metrics, and feedback loops at prompt, agent, and workflow levels.
• Establish cost management strategies for LLM usage, incorporating budgeting, rate limiting, caching, and optimization.
• Partner with AI and product engineers to safely and incrementally productionize AI features.
• Define and enforce guardrails for security, privacy, and data handling within 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 various 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.

Key skills/competency

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