Sierra
Software Engineer, Agent - Public Sector
About this role
Sierra is hiring a Software Engineer to build production-grade AI agents for US government institutions following their FedRAMP High certification. You'll design and deploy agents that help citizens access services and enable officials to better serve constituents, while guiding the evolution of Sierra's core platform based on direct customer feedback.
What you'll do
- Design and build production-grade AI agents for Federal and state government institutions
- Partner with industry leadership on public sector team building, capture strategy, and scaling
- Work directly with government institutions to understand needs and identify expansion opportunities
- Surface unmet customer needs and prototype new tools to guide platform evolution
- Collaborate with research, product, and platform teams on AI agent development
- Coordinate with public servants to understand workflows and solve real-world problems
What they're looking for
- Full stack software development
- AI development and machine learning processes
- Conversational or long-horizon agent frameworks
- Customer discovery and direct engagement
- Platform design and architecture
- Government procurement and regulatory knowledge
- Problem-solving in ambiguous environments
- 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.
View all jobs at SierraLikely interview questions
- Describe your experience building conversational or long-horizon AI agents—what frameworks did you use and what were the key challenges?
- Tell us about a time you worked directly with customers to understand their needs and translate that into product decisions.