Applied AI Engineer, Internal Automation

Location: Remote, based anywhere in the US; requires up to 15 days of travel per year for company meetings and other activities.

Salary: $150,000–$170,000 per year, depending on experience.

Company

hirify.global provides an AI-powered marketplace of skill-building courses, learning pathways, compliance content, and LMS/LXP integrations for organizations.

What you will do

• Build modular tools, intelligent workflows, internal automation components, and production-grade Retrieval-Augmented Generation systems.
• Turn validated AI proofs-of-concept into stable, maintainable production systems for internal business operations.
• Partner with AI Champions in Sales, Marketing, and Finance to identify workflow bottlenecks and create reusable self-service capabilities.
• Develop and maintain a scalable component library for AI-driven and traditional automation workflows.
• Improve CI/CD pipelines, reduce manual deployment steps, and strengthen release reliability.
• Implement infrastructure-as-code, monitoring, logging, and observability practices supporting 99.9% target uptime for critical internal APIs and workflows.

Requirements

• Proven ability to build scalable production systems from experimental AI concepts.
• Experience developing reliable automation frameworks and intelligent workflows.
• Ability to work with TDD, CI/CD, deployment processes, infrastructure-as-code, and operational monitoring.
• Strong partnership skills for collaborating with non-technical business groups and internal AI Champions.
• Must be based anywhere in the US and able to travel up to 15 days per year.

Culture & Benefits

• Remote-first work environment with several all-company in-person meetings each year.
• Comprehensive medical, dental, and vision coverage.
• Flexible paid time off, paid volunteer time off, and 401(k) matching.
• Incentive stock options and a professional development program with dedicated annual learning time.
• Hands-on use of AI tools across the organization with training and guardrails for responsible experimentation.