Role DescriptionIn this role you'll work with the team that is building cutting-edge agentic AI chat assistants that help our customers resolve issues faster and more effectively. This is a hands-on role where you'll own features end-to-end, from design through deployment and production support.
This role requires a strong understanding of microservice architecture, RESTful APIs, design patterns, and distributed systems, along with the emerging AI trends and expertise in modern AI systems.
In this role, you'll:- Drive the full lifecycle of agent implementation, from initial design to a live, high-performing production environment using RAG, MCP, and agentic architectures
- Test, evaluate, and fine-tune AI agents, RAG pipelines, and Model Context Protocol (MCP) implementations to hit strict performance targets using real-world data.
- Implement and optimize REST APIs for seamless system integration
- Debug, troubleshoot, and support production systems in real-time
- Define and enforce best practices for system design, including scalability, fault tolerance, and performance optimization.
- Work closely with frontend developers, DevOps engineers, product managers, and other stakeholders to deliver end-to-end solutions that meet business requirements.
- Create and maintain technical documentation for architecture, APIs, and processes to facilitate knowledge sharing and onboarding.
- Deploy and manage containerized services on Google Kubernetes Engine (GKE) or Cloud Run.
- Implement deep tracking and monitoring for non-deterministic AI agent behaviors using Google Cloud Observability (Stackdriver) or OpenTelemetry.
At a minimum, we would like you to have:- 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).
It's preferred if you have:- 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.
The US base salary range for this full-time position is between $176,800 - $194,500 + bonus + benefits. As pay varies by location, your recruiter will share more about the specific salary range for your targeted location during the hiring process.
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