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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
Requirements
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
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