Anthropic
Applied AI Engineer, Enterprise Tech
About this role
Anthropic seeks an Applied AI Engineer to serve as a trusted technical advisor for enterprise customers adopting Claude API into their products. You'll guide customers through architecture design, evaluation frameworks, and LLM implementation patterns while collaborating across Sales, Product, and Engineering teams.
What you'll do
- Advise enterprise customers on technical discovery, architecture decisions, and deployment of Claude-powered products
- Develop customized pilots, prototypes, and evaluation suites to influence customer product strategy
- Lead technical workshops and code reviews with customer engineering teams
- Partner with account executives to translate business requirements into technical solutions
- Document and create scalable assets on LLM prompting, evaluation, agentic systems, and architecture patterns
- Travel to customer sites for implementation support and conduct speaking engagements at industry conferences
What they're looking for
- Advanced prompt engineering and LLM production experience
- Agent development and evaluation frameworks
- Python or TypeScript proficiency
- Architecture design and technical mentoring
- Cross-functional collaboration and communication
- Model Context Protocol (MCP) and deployment at scale
- Problem-solving in ambiguous environments
- Technical writing and public speaking
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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 time you deployed an LLM application to production—what challenges did you face with prompting or evaluation?
- Describe your experience architecting systems with agents or agentic frameworks. How did you evaluate their performance?