About this role
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 WillBuild 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 BringYou 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