Lead Engineer

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. Role Summary We are seeking a Lead Engineer, AI Systems to serve as the technical anchor across our AI-augmented engineering organisation. This role bridges our Senior AI Coding Engineers, the AI/ML Developer (SLM Specialist), and the Agentic Systems intern cohort. You will set technical direction, own cross-cutting architectural decisions, and champion the responsible, high-impact adoption of AI-assisted development practices across the entire engineering function. You will remain hands-on with code while simultaneously shaping how the wider team builds, evaluates, and ships AI-powered products. Key Responsibilities 1 Technical Vision & Architecture Define and own the end-to-end technical architecture for AI Systems — spanning product feature surfaces, model inference APIs, and agentic toolchains. Drive Architecture Decision Records (ADRs), system design reviews, and RFC processes across squads. Establish standards for integrating SLM/LLM model endpoints into product surfaces built by the Senior Engineering team. Evaluate emerging AI infrastructure patterns (RAG, agentic orchestration, vector stores, model serving) and guide adoption decisions. Own the technical roadmap for AI tooling, developer productivity, and model integration layers. 2 Cross-Squad Technical Leadership Act as the primary technical liaison between the AI/ML Developer (model side) and Senior Engineers (product side), ensuring smooth API contracts, evaluation loops, and deployment handoffs. Provide technical direction to Agentic Systems interns, reviewing designs, code, and agentic pipeline implementations. Unblock senior engineers on hard architectural or integration challenges that span squad boundaries. Run cross-team design reviews, architecture syncs, and engineering guild sessions. 3 AI-Augmented Engineering Excellence Define and maintain the organisation’s standards for AI-assisted development — covering context engineering, AI code review protocols, context management, and tool evaluation criteria. Maintain and evolve the internal AI tooling playbook (Cursor IDE, Claude Code, Codex CLI, and emerging tools). Evaluate new AI coding tools, agentic frameworks (LangChain, LlamaIndex, CrewAI, AutoGen), and developer-productivity platforms; produce adoption recommendations with measured trade-offs. Conduct structured audits of AI-generated code across squads for correctness, security, and maintainability. 4 Hands-On Engineering Remain an active contributor: own critical-path features, prototype architectural spikes, and build shared infrastructure components used across squads. Personally drive resolution of the most complex production incidents and root-cause analyses. Review and merge high-impact PRs; maintain the highest code review quality bar on the team. Own observability and reliability for AI inference and MLOps integration layers in production. 5 Mentoring & Talent Development Mentor Senior Engineers, the AI/ML Developer, and interns through technical coaching, design feedback, and stretch assignments. Lead hiring panels and technical interviews; help define and uphold the engineering hiring bar. Contribute to onboarding frameworks that embed AI-first practices from day one. Model a culture of rigorous experimentation, psychological safety, and continuous improvement. 6 Stakeholder & Prod