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CamberCamberNew York, NY

AI Product Engineer

Builds and ships AI-powered products and tools integrating LLMs into workflows, owning end-to-end from prototype to production. Requires 3+ years backend/full-stack experience with strong Python skills and autonomous problem-solving in a fast-paced environment.

190k – 230k/yr
On-site3+ YOEFullstack Engineering

About the role

What you'll do

  • Rapidly prototype and ship AI-powered solutions to validate ideas and drive real impact
  • Own problems end-to-end from ambiguous idea to production system with minimal oversight
  • Build AI-powered products and tools that automate workflows, support decision making, and improve internal and customer-facing experiences
  • Operate across the stack including backend services, APIs, data pipelines, and lightweight product surfaces
  • Integrate AI capabilities such as LLMs into real workflows, ensuring they are reliable, observable, and cost-efficient
  • Run fast feedback loops by testing, measuring, and iterating using evaluation frameworks and real usage data
  • Develop reusable patterns and tooling that enable faster experimentation and iteration across the team

What we're looking for

Experience:

  • 3+ years of experience building production systems as a backend or full-stack engineer
  • Strong Python proficiency: comfortable building production systems and data workflows in Python
  • Highly autonomous: able to take loosely defined problems and independently drive them to outcomes
  • Strong experimentation and analytical approach: able to design experiments, construct evaluation frameworks using historical data, and iterate quickly based on results
  • Generalist builder: comfortable working across product engineering, data, and infrastructure, with a focus on shipping practical solutions rather than research or theory
  • Customer and product intuition: able to understand workflows and pain points to build useful systems
  • Curiosity: motivated to learn new tools, frameworks, and domains quickly, especially in a fast-evolving AI landscape

Nice to have:

  • Experience integrating LLMs or AI capabilities into production systems
  • Familiarity with evaluation tools such as Arize Phoenix or Promptfoo
  • Experience building agent-based workflows using frameworks like LangGraph
  • Familiarity with orchestration tools such as Temporal
  • Exposure to AI observability and tracing standards such as OpenInference
  • Familiarity with cloud platforms such as AWS

Skills

PythonLLMsAPIsData PipelinesAWSLangGraphTemporalArize PhoenixPromptfooOpeninference

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