AI Engineer, Agentic Workflows

Description:

Papaya Global is a rapidly growing, award-winning B2B tech unicorn with an ambitious mission to revolutionize the payroll & payments industry. With over $400M raised from multiple tier-one investors, our innovative technology provides a comprehensive solution for managing global workforces, encompassing everything from hiring and onboarding to managing and paying employees in over 160 countries.

We're building AI to tackle one of the hardest operational challenges in business: getting workforce management right across many markets, each with its own tax rules, labor laws, payment rails, and constantly shifting policies.

In this role on our AI team, you'll focus on agentic workflow automation. You'll design, build, and deploy reliable AI agents that automate complex HR, payroll, and payment workflows, handling decision-making, document intelligence, and complex tasks that typically require human judgment.

What You'll Do

• Design and build agent-based systems that automate document processing, business insights, and complex enterprise workflows
• Build observability, evaluation, and feedback loops for agent behavior to improve reliability, accuracy, and trust in production
• Own the technical architecture and engineering standards for agentic systems
• Build or manage data pipelines to process large volumes of documents and unstructured data
• Collaborate with domain experts to identify high-value automation targets and deliver end-to-end solutions
• Stay current on agent frameworks, LLM capabilities and limitations, and apply emerging patterns pragmatically in production

• 4+ years of experience in software engineering, AI/ML engineering, or a similar role with strong engineering fundamentals
• Experience building or integrating production LLM systems, AI agents, or workflow automation solutions
• Strong Python skills and solid software engineering principles
• Familiarity with orchestration and agent frameworks such as LangGraph, OpenAI/Claude Agents SDK, or similar tools
• Strong understanding of prompting, retrieval, tool use, and orchestration patterns, including their limitations in production
• Experience with cloud platforms and containerized deployments
• High ownership, strong problem-solving skills, and comfort working in ambiguous environments
• BS/MS in Computer Science, Engineering, Data Science, or equivalent practical experience