Backend Engineer, AI Systems
$176,800–$194,500 year
On-siteSunnyvale, California, United States
Job Summary
Drive the full lifecycle of agent implementation, from initial design to live, high-performing production environments using RAG, MCP, and agentic architectures. Test, evaluate, and fine-tune AI agents and RAG pipelines to hit strict performance targets while implementing and optimizing REST APIs for seamless system integration. Debug, troubleshoot, and support production systems in real-time, defining best practices for scalability and fault tolerance. Deploy containerized services on Google Kubernetes Engine or Cloud Run and implement deep tracking for non-deterministic AI behaviors using Google Cloud Observability. Work closely with frontend developers, DevOps engineers, and product managers to deliver end-to-end solutions, creating technical documentation for architecture and processes. This role is not eligible for immigration sponsorship.
Required Qualifications
- Bachelor's degree in Computer Science, Information technology, a similar engineering discipline, or equivalent practical experience
- 5 years of software design and full stack development experience
- Python programming skills with direct production experience
- Practical experience building with LLMs, AI agents, and RAG pipelines, or a demonstrable ability to go deep on new technical frameworks (like MCP) incredibly fast
- Understanding of LLM behavior, limitations and failure modes in production contexts
- Direct experience with GCP (Google Cloud Platform)
Desired Qualifications
- Experience with the Backend-for-a-Frontend design pattern
- Familiarity with feature flags or configuration management for AI system rollouts
- Familiarity with LLM prompt optimization, prompt patterns, and guardrails against prompt injection/hallucinations
- Understanding of context engineering and how to structure information for LLM consumption
- Experience using AI coding tools like AntiGravity, Claude, Cursor etc
- Experience with Java or Kotlin
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