Applied AI Engineer

Anthropic Anthropic · AI Frontier · Tokyo, Japan · Sales

Applied AI Engineer role focused on being a technical advisor to customers adopting Claude LLMs. Responsibilities include guiding architecture design, developing evaluation frameworks, and advising on implementation patterns for LLMs via API. Requires production experience with LLMs, strong Python skills, and expertise in common LLM implementation patterns.

What you'd actually do

  1. Serve as a technical advisor to Anthropic customers as they deploy new products & workflows with our models: from discovery through deployment, coordinating internally across multiple teams to drive customer success
  2. Partner with account executives to deeply understand customer product requirements and architect technical solutions, ensuring alignment between business objectives and technical implementation
  3. Guide technical architecture decisions and help customers build state-of-the-art products & workflows with LLMs via API
  4. Develop customized pilots, prototypes, and evaluation suites that make the case for customer deployment of our models into customer products and workflows via our API
  5. Lead hands-on technical workshops and code reviews with customer engineering teams

Skills

Required

  • 4+ years of experience in a technical roles such as Customer Engineer, Forward Deployed Engineer, Software Engineer or Technical Product Manager
  • Production experience with LLMs
  • advanced prompt engineering
  • agent development
  • evaluation frameworks
  • deployment at scale
  • Strong programming skills with proficiency in Python
  • experience building production applications
  • Expertise working with common LLM implementation patterns
  • prompt engineering
  • evaluation frameworks
  • agent frameworks
  • retrieval frameworks
  • Ability to navigate ambiguity and execute across domains
  • High cooperation mindset for cross-organizational collaboration
  • Fluent in Japanese and English

Nice to have

  • Passion for advancing safe, beneficial AI systems through creative technical applications

What the JD emphasized

  • Production experience with LLMs
  • evaluation frameworks
  • agent development
  • deployment at scale
  • common LLM implementation patterns
  • agent frameworks
  • retrieval frameworks

Other signals

  • customer adoption
  • LLM implementation patterns
  • technical advisor