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Principal AI Product Engineer at LinkedIn
Dubai, Dubai, United Arab Emirates -
Full Time

Start Date

Immediate

Expiry Date

27 Dec, 26

Salary

25000.0

Posted On

28 Sep, 26

Experience

16 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

Industry

Information Technology & Services

Description
  • Discover the Responsibilities:Own the end-to-end architecture and delivery of production AI products, from AI backend and APIs through to user-facing applications.
  • Design and establish scalable RAG architectures and Agentic AI workflows, including tool use, planning and human-in-the-loop patterns.
  • Define AI-native engineering practices, including the use of coding agents, reusable components, development frameworks and evaluation tooling.
  • Build and guide the development of production backend services using Python and modern API technologies.
  • Develop reliable AI applications that effectively handle streaming, failures, latency and other real-world production requirements.
  • Establish strong evaluation practices across grounding, retrieval quality, regression detection and production performance.
  • Define reusable technical standards, reference architectures and components that can be adopted across multiple AI products.

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Responsibilities
  • Discover the Responsibilities:Own the end-to-end architecture and delivery of production AI products, from AI backend and APIs through to user-facing applications.
  • Design and establish scalable RAG architectures and Agentic AI workflows, including tool use, planning and human-in-the-loop patterns.
  • Define AI-native engineering practices, including the use of coding agents, reusable components, development frameworks and evaluation tooling.
  • Build and guide the development of production backend services using Python and modern API technologies.
  • Develop reliable AI applications that effectively handle streaming, failures, latency and other real-world production requirements.
  • Establish strong evaluation practices across grounding, retrieval quality, regression detection and production performance.
  • Define reusable technical standards, reference architectures and components that can be adopted across multiple AI products.