AI System Architect

About Payoneer

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.

WHAT YOU WILL DO?

• Design, build, and ship production-grade AI agents (LLM-powered) that automate and augment Customer Journey workflows - support, operations, onboarding, disputes, and KYC.
• Partner with stakeholders across Customer Journey, Product, Tech, Risk, and Compliance to scope problems, prioritize use cases, and align agent solutions with business goals.
• Develop agents end-to-end: prompt design, tool / function calling, retrieval (RAG) over internal knowledge, memory, multi-step reasoning, and graceful fallbacks.
• Integrate agents with internal systems (CRM / ticketing, knowledge bases, data platforms, payment systems) via APIs, and event pipelines.
• Establish evaluation and quality gates - offline evals, A/B tests, human-in-the-loop review, tracing and observability - to ensure agents are accurate, safe, and policy-aligned.
• Champion responsible AI: build guardrails for PII, fraud, compliance, and hallucinations; ensure traceability, auditability, and safe hand-off to humans.
• Drive adoption across Customer Journey teams: enable users, gather feedback, iterate quickly, and measure business impact (CSAT, AHT, deflection, accuracy, cost-to-serve).
• Stay ahead of the agentic AI ecosystem (models, orchestration frameworks, evals, tooling) and bring proven practices into Payoneer.
  
  

• Technical background: BSc in Computer Science, Software Engineering, Data Science, Industrial Engineering, or another quantitative or engineering discipline.
• Software engineering experience: 3+ years of hands-on experience building, deploying, and maintaining production-grade software using Python, TypeScript/Node.js, or similar languages.
• AI agents and LLM applications: 2+ years of practical experience developing and deploying LLM-powered applications and AI agents, including prompt engineering, tool/function calling, RAG, and agent orchestration.
• Agent frameworks: Hands-on experience with at least one LLM or agent development framework, such as LangChain/LangGraph, LlamaIndex, OpenAI Agents SDK, Anthropic SDK, Semantic Kernel, AutoGen, or an equivalent framework.
• API and system integrations: Strong experience designing and implementing REST APIs, webhooks, and event-driven integrations, connecting multiple services, systems, and data sources.
• Data and retrieval: Experience with SQL, embeddings, vector databases, and building retrieval pipelines across structured and unstructured data.
• Production deployment: Experience deploying applications to cloud environments (AWS, GCP, or Azure) and working with CI/CD pipelines. Familiarity with LLM evaluation, monitoring, observability, and safety mechanisms is a plus.
• Builder mindset: A hands-on, execution-oriented approach, with a track record of taking solutions from concept to production in fast-paced, iterative, 0-to-1 environments.
• Analytical thinking: Strong problem-solving skills and an experimentation-driven approach to debugging, evaluating, and improving AI-powered solutions.
• Product and business orientation: Ability to translate business needs into technical solutions, make sound engineering trade-offs, and connect technical decisions to measurable business outcomes.
• Communication and collaboration: Ability to explain technical concepts, agent behavior, limitations, and trade-offs clearly to non-technical stakeholders.
• Ownership and organization: Proactive, detail-oriented, and comfortable managing multiple technical workstreams while collaborating with cross-functional teams.
• Global mindset: Experience working in an international environment and fluency in English.
• Continuous learning: Curiosity and enthusiasm for emerging AI technologies, agentic architectures, and best practices in production AI development.
• Customer support technology: Familiarity with customer support platforms or contact center tools is an advantage.