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Sierra

Software Engineer, Agent - Retail

San Francisco, CAfulltimemidAdded 2 days ago

About this role

Join Sierra's Retail Agent Engineering team to design and deploy production-grade AI agents for major consumer brands like Gap, Wayfair, and Sonos. You'll own the complete agent development lifecycle—from prototyping through production deployment—building scalable systems that drive commerce, resolve customer issues, and shape the future of retail AI.

What you'll do

  • Design, build, and ship production-grade AI agents that power retail experiences across web, app, phone, and in-store channels
  • Own the complete Agent Development Life Cycle from pilot through deployment and continuous iteration in production
  • Partner directly with enterprise customers and startups to understand business challenges and build transformative AI solutions
  • Modernize complex commerce systems by creating scalable packages for order management, fulfillment, returns, and warranties
  • Surface customer needs and prototype new platform features to guide Sierra's core product evolution
  • Set standards for AI agent development best practices across the organization

What they're looking for

  • AI/LLM agent architecture and development
  • Production system design and scalability
  • Python or similar backend languages
  • API integration and system design
  • Commerce/e-commerce domain knowledge
  • Customer collaboration and requirements gathering
  • Full-stack product thinking
  • Debugging and performance optimization
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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 your experience building and deploying AI agents in production environments—what challenges did you face?
  • Describe a complex system integration you've led. How did you handle abstraction and scalability?
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