Applied AI Engineer

About the role

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

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

Requirements

• 2-5 years of professional experience in software engineering, AI engineering, or a related technical role
• Production Python experience with real shipped products, tools, integrations, or automations (not just notebooks or coursework)
• Practical understanding of LLM APIs: prompt construction, context management, token economics, and tool-use patterns
• Ability to decompose messy problems into clean components with well-defined interfaces
• Experience integrating systems via APIs: reading documentation, handling auth, managing rate limits, and addressing edge cases
• Quality instinct: naturally asking "how do I know this is working?" and "how will I know when it breaks?"
• Comfort with operational ownership: monitoring, reviewing outputs, and maintaining AI systems in production
• Ability to quickly learn new frameworks, APIs, and domains
• Clean, documented code that others can read, understand, and extend

This role is not:

• A research role (no model training or publishing papers)
• A data engineering role (you'll consume data, not build pipelines)
• A DevOps or infrastructure role (you'll deploy agents but not manage servers or CI/CD)
• A solo project (you'll collaborate closely with the Data & AI team and stakeholders)

Benefits

• Competitive pay and equity with significant upside
• Flexible schedules and time off
• Sabbatical after every five years of service
• Ping-pong table and free snacks