Principal Engineer

Founded in 2005, Payoneer is the global financial platform that removes friction from doing business across borders, with a mission to connect the world’s underserved businesses to a rising global economy. We’re a community with over 2,500 colleagues all over the world, working to serve customers, and partners in over 190 countries and territories.

By taking the complexity out of the financial workflows–including everything from global payments and compliance to multi-currency and workforce management, to providing working capital and business intelligence–we give businesses the tools they need to work efficiently worldwide and grow with confidence. Role Summary

We’re looking for a Principal Architect who is a tech enthusiast with a drive for excellence and an out-of-the-box mindset to satisfy business needs in a complex payments environment. You will be a key technical leader in our Cloud Modernization initiative while also accelerating how we use AI to improve engineering velocity, reliability, and customer outcomes by enabling safe, governed adoption of GenAI, building agentic systems, and embedding AI-assisted capabilities into our platforms and workflows.

What You’ll Do

· You will introduce AI-enabled capabilities to solve engineering and business problems by identifying high-value use cases, enabling AI adoption and AI-led development practices across teams, and delivering measurable outcomes.

· You will build and ship agentic systems that can plan, execute, and collaborate with humans safely with robust controls, and help build core AI capabilities to scale adoption.

Who You Are

· You also have a strong point of view on practical AI adoption. You can translate real engineering pain points into AI-enabled solutions, select the right patterns and ship systems with strong evaluation, observability, and governance.

Key Skills and Competencies

· Experience in designing and shipping LLM-enabled applications and agentic architectures in production environments, with sound judgment on when not to use agents.

· Experience in responsible AI governance, evaluation, and data safety, including prompt‑injection mitigation, data access controls, secrets handling, policy enforcement, audit logging, offline/online evaluation, observability, red‑teaming, cost/latency monitoring, and safe handling of enterprise and PII data.