Sierra
Software Engineer, Agent - Travel & Hospitality
About this role
Sierra is hiring a Software Engineer to design and deploy production-grade AI agents for the Travel & Hospitality sector, working with Fortune 500 airlines, hotel chains, and travel companies. You'll own the full agent development lifecycle—from pilot to production—while directly shaping the platform's evolution through customer partnerships and real-world problem-solving.
What you'll do
- Design, build, and deploy production-grade AI agents that drive revenue and solve critical customer challenges
- Own the complete Agent Development Life Cycle (ADLC) from concept through deployment and continuous optimization
- Partner directly with enterprise leaders and startups to understand business needs and deliver agent solutions
- Prototype multimodal experiences integrating voice, chat, and visual interfaces for travel and hospitality use cases
- Surface customer insights and unmet needs to guide Sierra's core platform evolution
- Collaborate with research, product, and platform teams to define best practices and shape future tools
What they're looking for
- AI/ML systems design and deployment
- Full-stack software engineering for production environments
- Agent orchestration and prompt engineering
- API integration (e.g., Google Maps, payment systems, travel APIs)
- System design and scalability optimization
- Customer collaboration and requirements gathering
- Multimodal AI (voice, chat, visual interfaces)
- Problem-solving across complex business domains
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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.
View all jobs at SierraLikely interview questions
- Walk us through your experience building and shipping production AI systems—what made them reliable and performant at scale?
- Describe a time you owned a project end-to-end from conception to deployment. How did you handle ambiguity and iterate based on real-world feedback?