AI Operations Engineer

Details

ClickHouse is building a newly formed AI Engineering function within IT Operations to centralize AI Operations across the company. The team will apply the rigor of IT Operations to the design, deployment, and lifecycle management of AI solutions and agents.

This hands-on role will help design, build, and maintain AI-driven solutions for People, Finance, Legal, Engineering, and other business functions. Responsibilities include building integrations, maintaining agents, supporting AI cost visibility, evaluating models, and training the organization on AI best practices.

Responsibilities

Build AI-Powered Solutions

• Develop and deploy solutions using LLMs, automation frameworks, and internal tooling for business and engineering use cases.
• Integrate AI into existing systems, including HRIS, ATS, ERP, procurement orchestration, ticketing, CI/CD, developer tooling, CLM, and Matter Management infrastructure.
• Build workflows for document generation, data extraction, knowledge retrieval, and decision support across systems and knowledge bases.
• Use modern integration standards such as MCP to provide AI models and agents with direct, secure access to internal knowledge and context.

Support Model Evaluation

• Test and benchmark AI models against accuracy, latency, cost, and safety criteria.
• Support model risk assessments and due diligence with Security and GRC.
• Help implement and maintain the internal model routing solution.

Support Cost Visibility

• Build dashboards and reporting for AI and LLM spending across teams and tools.
• Implement tagging, monitoring, and alerting for cost anomalies.
• Identify and execute cost-optimization opportunities, including prompt efficiency, caching, and model right-sizing.

Maintain Agent Infrastructure

• Provision, credential, and de-provision AI agent access.
• Support the deployment, versioning, monitoring, and retirement of agents.
• Maintain and patch agent infrastructure and dependencies.
• Maintain audit trails and logging for agent actions.

Deliver Training and Enablement

• Design and deliver company-wide AI training programs for non-technical business users and engineers.
• Create playbooks, guides, templates, and reusable components for safe and effective AI adoption.
• Run onboarding sessions, office hours, and workshops to build AI fluency and drive adoption of approved tools and agents.
• Establish feedback loops with users to improve training content, tooling, and documentation.

Success Measures

• Reduced redundant or inefficient AI tool and model spending through right-sizing, routing, and cost visibility.
• Adoption of a framework for evaluating and selecting models and tools based on cost, latency, accuracy, and risk.
• AI agents and workflows built, deployed, and maintained across business and engineering functions.
• Improved organization-wide AI fluency and literacy, including reduced shadow-AI usage.
• Identity, access, audit, and lifecycle practices for AI agents that meet Security and GRC requirements.

Requirements

• 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, including AWS, GCP, or Azure, and experience hosting or operating services in production.
• Experience comparing and evaluating AI models across providers, including cost, latency, and quality trade-offs.
• Experience using usage and billing data for cost visibility, forecasting, or optimization.
• Ability to understand business and engineering workflows and translate them into technical solutions.
• Experience working with or integrating business systems and engineering or developer tooling.
• Experience creating training materials, documentation, or workshops for mixed technical and non-technical audiences.
• Understanding of data privacy, security, and compliance considerations.
• Experience implementing AI safeguards, including identity and access controls for automated or non-human actors.

Compensation

• Typical starting salary in the United States: $170,000–$220,000 USD per year.
• Typical starting salary in US premium markets: $200,000–$250,000 USD per year.
• Premium market ranges may apply in locations such as the San Francisco Bay Area and the New York City Metro Area.

Benefits

• Flexible, remote-friendly work environment.
• Employer healthcare contributions.
• Stock options for new team members.
• Flexible time off in the United States and generous entitlement in other countries.
• $500 home office setup allowance for remote employees.
• Opportunities to participate in company-wide global gatherings and offsites.