Anthropic
AI Operations Engineer, Partnerships
About this role
Anthropic seeks an AI Operations Engineer to design and deploy Claude-powered agentic workflows that automate partner experience operations. You'll build automations for application triage, onboarding, communications, and reporting—connecting them seamlessly with existing partner systems and CRM infrastructure.
What you'll do
- Design and build agentic workflows using Claude, MCP connectors, and API integrations to automate partner operations
- Integrate workflows with partner portal, CRM, enablement stack, finance systems, and databases
- Convert recurring manual work into monitored, tested automations with human-in-the-loop checkpoints where needed
- Own operational health: error handling, edge cases, escalation paths, and iteration as processes change
- Rapidly prototype workflows with partner managers to validate requirements in days
- Develop reusable patterns, prompts, and connectors to accelerate future workflow builds
What they're looking for
- LLM and agentic tooling (prompt design, tool calling, MCP frameworks)
- API integration and business systems (Salesforce, CRM, PRM portals, LMS, Slack, email)
- Python or JavaScript scripting
- Workflow design and process automation
- Agent output evaluation and quality assessment
- Risk assessment and human-in-the-loop design
- Data modeling and systems integration
- Production monitoring and operational ownership
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Anthropic
Anthropic builds Claude, an AI assistant, and is hiring for engineering roles across infrastructure, data systems, and security that support both AI research operations and the company's internal technology needs. The company seeks infrastructure engineers, systems integrators, data scientists, and security specialists to build production-scale systems for training data pipelines, financial operations, developer productivity measurement, research infrastructure, and server firmware security.
- Website
- anthropic.com
Likely interview questions
- Walk us through a complex automation you've built with LLMs—how did you handle edge cases and when did you add human checkpoints?
- Tell us about a time you integrated multiple business systems via APIs—what was the toughest integration challenge and how did you solve it?