About the Team IT Operations at ClickHouse keeps the company's internal systems, tooling, and infrastructure running securely and efficiently — from identity and endpoint management to the platforms every team relies on to do their work. This role sits on a newly formed AI Engineering function within IT Operations, created to centralize AI Ops rather than leave it disjointed across teams. The team — a Technical Lead plus one or two AI Ops Engineers — reporting to the IT Business Systems Manager, and brings the same rigor IT Ops applies to systems and infrastructure to the design, deployment, and lifecycle management of AI solutions and the agents they power. About the Role We are looking for an AI Ops Engineer to help build and run a new AI Ops function at ClickHouse. You will work under a Technical Lead to design, build, and maintain AI-driven solutions across the business (People, Finance, Legal, and beyond) and engineering — building integrations, maintaining agents, supporting cost visibility, and helping train the org on AI best practices. This is a hands-on execution role: you will move quickly, ship real workflows, and help the team learn what works as we centralize AI Ops for the first time. Success This is a net-new function; specific goals will be set with your Technical Lead. Directionally, the team is measured on: - Measurable reduction in redundant or inefficient AI tool and model spend through right-sizing, routing, and cost visibility - A working framework for evaluating and selecting models/tools by cost, latency, accuracy, and risk, adopted across teams - Agents and AI workflows built, deployed, and maintained cross-functionally (People, Finance, Legal, Engineering, and beyond) - Improved AI fluency and literacy org-wide, measured through training participation, adoption of approved tools, and reduced shadow-AI usage - Established identity, access, audit, and lifecycle practices for AI agents that satisfy Security and GRC requirements YOU WILL Build AI-Powered Solutions - Develop and deploy solutions using LLMs, automation frameworks, and internal tooling, for both business and engineering use cases - Integrate AI into existing systems (HRIS, ATS, ERP, procurement orchestration, ticketing, CI/CD, developer tooling) and legal infrastructure like CLM or Matter Management - Build workflows such as document generation, data extraction, knowledge retrieval, and decision support across systems and knowledge bases - Use modern integration standards (e.g., MCP) to give AI models and agents direct, secure access to internal knowledge and context Support Model Evaluation - Help test and benchmark AI models against accuracy, latency, cost, and safety criteria - Support model risk assessments and due diligence alongside Security and GRC - Help implement and maintain the internal model routing solution Support Cost Visibility - Help build dashboards and reporting for AI/LLM spend across teams and tools - Implement tagging, monitoring, and alerting for cost anomalies - Identify and execute on cost-optimization opportunities (prompt efficiency, caching, model right-sizing) Maintain Agent Infrastructure - Help provision, credential, and de-provision AI agent access - Support deployment, versioning, monitoring, and retirement of agents - Maintain and patch agent infrastructure and dependencies - Help maintain audit trails and logging for agent actions Deliver Training and Enablement - Design and deliver company-wide AI training programs tailored to different audiences — non-technical business users through engineers - Create lightweight playbooks, guides, templates, and reusable components for safe, effective AI adoption - Run onboarding sessions, office hours, and workshops to build AI fluency and drive adoption of approved tools and agents - Establish a feedback loop with users to continuously improve training content, tooling, and documentation YOU BRING You are not expected to have every skill below — tell us which are your strongest. - Experience building with modern AI/ML tools and frameworks - Experience building and consuming APIs, developing data pipelines, and architecting system integrations - Working knowledge of cloud infrastructure (AWS, GCP, or Azure) and experience hosting or operating services in production - Experience comparing and evaluating AI models across providers, including trade-offs between cost, latency, and quality - Comfort working with usage and billing data to build cost visibility, forecasting, or optimization - Ability to understand both business and engineering workflows and translate them into technical solutions - Experience working with or integrating business systems as well as engineering/developer tooling - Experience creating training materials, documentation, or workshops for mixed technical/non-technical audiences - Understanding of data privacy, security, and compliance considerations - Experience implementing safeguards in AI systems, including identity and access controls for automated/non-human actors