Client Solutions Operations Specialist supporting TubeScience’s paid social growth engine. Preparing executive - ready materials and coordinating cross - functional execution for DTC and Fortune 500 clients.
About the role
- 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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