Zello
Applied AI Engineer
About this role
Zello seeks an Applied AI Engineer to build and ship production AI agents end-to-end, from prototype through deployment and ongoing optimization. You'll own the full lifecycle of AI tools integrated with Zello's systems, managing quality, monitoring performance, and driving continuous improvement for a voice-first communication platform serving 175+ million users.
What you'll do
- Develop production-grade AI agents and automations integrated with LLM APIs and Zello's existing systems (Slack, Jira, HubSpot, Snowflake)
- Write and maintain production Python code for prompt construction, response handling, and tool-use patterns
- Build evaluation harnesses with automated quality scoring and regression detection for deployed agents
- Monitor agent performance in production, triage failures, and implement improvements based on real usage data
- Manage human reinforcement workflows and feedback loops to tune agent behavior
- Create reusable code patterns, component libraries, and documentation to accelerate future development
What they're looking for
- Production Python development
- LLM API integration and prompt engineering
- System integration via APIs (authentication, rate limiting, error handling)
- Software architecture and system design
- Testing and monitoring practices
- Data integration with analytics platforms
- Problem decomposition and abstraction thinking
- Operational ownership and post-deployment maintenance
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Zello
Zello builds a push-to-talk communication platform serving over 175 million users globally. The company is hiring senior engineers across support, AI, and mobile development to scale their voice-first platform and modernize core infrastructure.
- Website
- zello.com
Likely interview questions
- Walk us through a production AI system or agent you've built end-to-end. How did you handle prompt engineering, context management, and ensuring quality after deployment?
- Describe your experience integrating third-party APIs (like Slack, Jira, or data platforms). How do you handle authentication, rate limiting, and edge cases in production?