AI Operations Engineer

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 HRIS, ATS, ERP, procurement, ticketing, CI/CD, developer tooling, CLM, and matter-management systems.
• Build workflows for document generation, data extraction, knowledge retrieval, and decision support.
• Use modern integration standards such as MCP to provide AI models and agents with secure access to internal knowledge and context.

Support Model Evaluation

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

Support Cost Visibility

• Build dashboards and reporting for AI and LLM spend.
• 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 deployment, versioning, monitoring, and retirement of agents.
• Maintain and patch agent infrastructure and dependencies.
• Maintain audit trails and logging for agent actions.

Training and Enablement

• Design and deliver AI training for technical and non-technical audiences.
• Create playbooks, guides, templates, and reusable components for safe AI adoption.
• Run onboarding sessions, office hours, and workshops.
• Establish feedback loops to improve training, tooling, and documentation.

Requirements

• Experience building with modern AI and machine-learning tools and frameworks.
• Experience building and consuming APIs, developing data pipelines, and architecting system integrations.
• Working knowledge of cloud infrastructure and experience hosting or operating production services.
• Experience comparing and evaluating AI models across providers, including cost, latency, and quality trade-offs.
• Experience using billing or usage data for cost visibility, forecasting, or optimization.
• Ability to translate business and engineering workflows into technical solutions.
• Experience integrating business systems and engineering or developer tooling.
• Experience creating training materials, documentation, or workshops for mixed 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 and Benefits

• ATS-listed salary: $170,000–$250,000 USD.
• Typical US starting salary: $170,000–$220,000 USD.
• Typical US premium-market starting salary: $200,000–$250,000 USD.
• Healthcare contributions.
• Company stock options.
• Flexible time off in the US and generous entitlement in other countries.
• $500 home-office setup benefit for remote employees.
• Opportunities to attend company-wide offsites.
• Flexible, remote-friendly work environment.