Applied AI Engineer

Zello•Austin, TX

About The Position

The AI & Data team has more high-value AI use cases than capacity to build them. Today, the team leads agent development directly alongside many other responsibilities, and this work needs a dedicated builder. This hire will be one of the first few Applied AI Engineers at Zello, responsible for taking AI agents from prototype to production and then owning their ongoing health: monitoring quality, managing human reinforcement workflows, and driving continuous improvement. After a successful first year, you will: Shipped at least 3 production-grade AI agents within your first 90 days that internal teams actively use (Slack-integrated agents, workflow automations, data-driven assistants) Built evaluation harnesses for deployed agents with automated quality scoring and regression detection Integrated AI tools with Zello's existing systems (Slack, Jira, HubSpot, Snowflake) via APIs, with proper logging and monitoring in place Established reusable code patterns and component libraries that make future agent development faster Taken ownership of deployed agent operations: monitoring performance, overseeing human reinforcement workflows, triaging failures, and driving measurable improvement in agent quality over time Independently scoped and shipped AI tools for new use cases, whether identified by stakeholders or discovered on your own What you'll do Build AI agents and automations end-to-end: from scoping the use case through deployment and ongoing maintenance Write production Python code that integrates LLM APIs (prompt construction, response handling, context management, tool use) into real workflows Connect AI tools with Zello's systems (Slack, Jira, HubSpot, Snowflake) through APIs, handling authentication, rate limits, error cases, and logging Monitor deployed agents in production: track quality metrics, triage failures, and ship improvements based on real usage data Manage human reinforcement operations: review agent outputs, maintain feedback loops, and tune agent behavior based on reinforcement signals Build and maintain evaluation harnesses that catch regressions and measure agent quality programmatically Create reusable components, patterns, and documentation that raise the bar for future development on the team Communicate clearly with technical and non-technical stakeholders about what you've built, what's working, and where things need attention

Requirements

2-5 years of professional experience in software engineering, AI engineering, or a related technical role.
Production Python experience with demonstrable projects (tools, integrations, automations, shipped products).
Practical understanding of LLM APIs, including prompt construction, context management, token economics, and tool-use patterns.
Ability to decompose complex problems into clean components with well-defined interfaces.
Experience integrating systems via APIs, including handling authentication, rate limits, and edge cases.
A strong quality instinct, including writing tests and building monitoring.
Comfort with operational ownership, including monitoring, reviewing outputs, and maintaining AI systems in production.
Ability to quickly learn new frameworks, APIs, and domains.
Clean and well-documented code that is easy for others to read, understand, and extend.

Responsibilities

Build AI agents and automations end-to-end: from scoping the use case through deployment and ongoing maintenance
Write production Python code that integrates LLM APIs (prompt construction, response handling, context management, tool use) into real workflows
Connect AI tools with Zello's systems (Slack, Jira, HubSpot, Snowflake) through APIs, handling authentication, rate limits, error cases, and logging
Monitor deployed agents in production: track quality metrics, triage failures, and ship improvements based on real usage data
Manage human reinforcement operations: review agent outputs, maintain feedback loops, and tune agent behavior based on reinforcement signals
Build and maintain evaluation harnesses that catch regressions and measure agent quality programmatically
Create reusable components, patterns, and documentation that raise the bar for future development on the team
Communicate clearly with technical and non-technical stakeholders about what you've built, what's working, and where things need attention