Senior/Staff Software Engineer: Agents
New
V
Virco TalentAI software
This is a fully remote opportunity open to candidates located in the United States or Canada.Full-TimeStaff
SalaryCompetitive base salary ($210K – $230K DOE)
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Job Details
- Experience
- 5+ years of professional software engineering experience
- Required Skills
- Node.jsPostgreSQLMongoDBTypeScriptReactMicroservicesDistributed Systems
Requirements
- Have 5+ years of professional software engineering experience.
- Have proven experience building and shipping production-grade AI/LLM-powered applications or agentic systems.
- Bring strong backend development expertise using Node.js and TypeScript.
- Have experience designing scalable distributed systems, microservices, and backend infrastructure.
- Have hands-on experience with tool calling, prompt engineering, agent loops, and AI orchestration frameworks.
- Be familiar with evaluation and observability tooling for non-deterministic AI systems, such as LangSmith, Braintrust, or custom evaluation frameworks.
- Have full-stack engineering experience with modern frontend frameworks such as React and TypeScript.
- Understand databases such as PostgreSQL and MongoDB.
- Have experience with RAG systems at production scale, preferred.
- Have experience with cloud infrastructure and modern deployment practices, preferred.
- Have experience mentoring engineers or leading technical initiatives, preferred.
Responsibilities
- Own and deliver AI agent experiences end-to-end, including backend orchestration, LLM integrations, conversational interfaces, tool execution, and evaluation systems.
- Design scalable architectures for agentic AI systems and contribute to platform-level AI infrastructure.
- Build and improve tool abstractions, retrieval pipelines and RAG systems, conversation persistence, evaluation frameworks, permissioning, and observability.
- Collaborate with Product, Design, and Engineering leadership to define technical direction and product strategy.
- Make engineering tradeoffs that balance latency, reliability, scalability, and cost efficiency.
- Improve AI system quality through experimentation, evaluation frameworks, and continuous iteration.
- Lead or contribute to large-scale technical initiatives across multiple teams.
- Mentor engineers and promote engineering best practices.
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