Applied AI Engineer

Anthropic Anthropic · AI Frontier · Sydney, Australia · Applied AI

Applied AI Engineer role focused on being a technical advisor to customers deploying Claude (LLM). Responsibilities include guiding architecture, developing evaluation frameworks, and implementing cutting-edge LLM patterns via API. Requires strong Python skills and production experience with LLMs, including agent development and retrieval frameworks.

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

  • 8+ years of experience in a technical roles such as Customer Engineer, Forward Deployed Engineer or Software Engineer
  • 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

Nice to have

  • customer-facing skills
  • technical Product Engineer
  • technical advisor to customers
  • architecture design decisions
  • developing evaluation frameworks
  • guiding customers through the most cutting edge implementation patterns for LLMs
  • technical discovery through successful deployment
  • deep engineering expertise
  • customer-facing skills
  • understand the potential of working with LLMs
  • build innovative solutions that address complex business challenges
  • maintaining our high standards for safety and reliability
  • Partner with account executives
  • architect technical solutions
  • alignment between business objectives and technical implementation
  • build state-of-the-art products & workflows with LLMs via API
  • Develop customized pilots, prototypes, and evaluation suites
  • make the case for customer deployment of our models into customer products and workflows via our API
  • Lead hands-on technical workshops and code reviews with customer engineering teams
  • Identify common design patterns and contribute insights back to our Product and Engineering teams
  • Maintain strong knowledge of the latest developments in LLM capabilities, implementation patterns, and AI product development stacks
  • Travel occasionally to customer sites for workshops, implementation support, and building relationships
  • Attend conferences, lead speaking engagements, write blog posts and white papers on topics surrounding the AI space
  • intellectual openness
  • finding simple solutions to complex problems
  • High cooperation mindset for cross-organizational collaboration
  • balancing competing priorities
  • integrity
  • Passion for advancing safe, beneficial AI systems through creative technical applications
  • Exceptional communication skills to convey technical concepts to diverse stakeholders
  • low ego
  • collaborative approach

What the JD emphasized

  • Production experience with LLMs
  • advanced prompt engineering
  • agent development
  • evaluation frameworks
  • deployment at scale
  • Strong programming skills with proficiency in Python
  • building production applications
  • common LLM implementation patterns
  • prompt engineering
  • evaluation frameworks
  • agent frameworks
  • retrieval frameworks

Other signals

  • customer-facing
  • deployment
  • LLM implementation patterns