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ClickHouse, a Forbes Cloud 100 company and leader in real-time analytics and data warehousing, is building a new AI Operations function within IT Operations. This role sits on a newly formed AI Engineering team (Technical Lead + 1-2 AI Ops Engineers) reporting to the IT Business Systems Manager.
As an AI Operations Engineer, you will be a hands-on executor building and running AI-driven solutions across the business and engineering. You'll work under a Technical Lead to design, build, and maintain AI agents and workflows that span People, Finance, Legal, Engineering, and beyond.
Key responsibilities include:
**Build AI-Powered Solutions**: Develop and deploy LLM-based solutions and automation frameworks for business and engineering use cases. Integrate AI into existing systems (HRIS, ATS, ERP, procurement, ticketing, CI/CD, legal infrastructure). Build workflows for document generation, data extraction, knowledge retrieval, and decision support using modern integration standards like MCP.
**Support Model Evaluation**: Test and benchmark AI models against accuracy, latency, cost, and safety criteria. Support model risk assessments alongside Security and GRC teams. Help implement and maintain internal model routing solutions.
**Support Cost Visibility**: Build dashboards and reporting for AI/LLM spend across teams and tools. Implement tagging, monitoring, and alerting for cost anomalies. Identify and execute cost-optimization opportunities through prompt efficiency, caching, and model right-sizing.
**Maintain Agent Infrastructure**: Provision, credential, and de-provision AI agent access. Support deployment, versioning, monitoring, and retirement of agents. Maintain audit trails and logging for agent actions.
**Deliver Training and Enablement**: Design and deliver company-wide AI training programs for diverse audiences. Create playbooks, guides, templates, and reusable components. Run onboarding sessions, office hours, and workshops to build AI fluency and drive adoption of approved tools.
Success is measured on reducing redundant AI spend, establishing a framework for model/tool evaluation, deploying cross-functional agents and workflows, improving org-wide AI literacy, and establishing identity, access, audit, and lifecycle practices for AI agents.
