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Sierra

Software Engineer, Agent - Retail

San Francisco, CAfulltimemidAdded today

About this role

Sierra seeks a Software Engineer to design and deploy production-grade AI agents for retail enterprises, handling mission-critical customer interactions across commerce platforms. You'll own the full agent development lifecycle—from pilot through production optimization—while partnering directly with major brands like Gap and Wayfair to solve real-world business challenges at scale.

What you'll do

  • Design, build, and ship production-grade AI agents that drive revenue and serve hundreds of millions of interactions
  • Own the complete Agent Development Life Cycle from initial pilot through deployment and continuous iteration
  • Work directly with enterprise customers to understand business challenges and translate them into scalable AI solutions
  • Integrate complex commerce systems (order management, fulfillment, returns) into cohesive agent workflows
  • Collaborate with platform, product, and research teams to shape Sierra's core AI agent capabilities
  • Build and optimize agents handling unprecedented traffic volumes during peak retail periods globally

What they're looking for

  • End-to-end production system design and scaling
  • AI/LLM systems development and deployment
  • Problem-solving in ambiguous, fast-moving environments
  • Direct customer engagement and requirements gathering
  • Technical communication across audiences
  • Systems integration and API work
  • Commerce platform knowledge
  • Prototyping and rapid iteration
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Sierra

Sierra builds production-grade AI agents for enterprise clients in insurance, financial services, and other industries, enabling companies to automate complex business processes and enhance customer experiences. The company is hiring Software Engineers to design and deploy mission-critical AI systems, develop platform infrastructure, and work directly with enterprise customers on agent development and optimization.

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Likely interview questions

  • Walk us through a production system you built end-to-end—what were the biggest scaling challenges and how did you solve them?
  • Describe your experience deploying LLM/AI systems in production. What went wrong and how did you handle it?
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