Lead Engineer - AI

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 & Product Collaboration

• Partner with the Director of Engineering and Product leadership to translate product strategy into a phased technical roadmap.


• Present architectural proposals and trade-off analyses to engineering leadership and executive stakeholders as required.


• Coordinate with DevOps/Platform teams on GPU/TPU compute provisioning, CI/CD for model pipelines, and cloud cost optimisation.

Required Qualifications

1  Education

• Bachelor’s or Master’s degree in Computer Science, or a related field.


• Candidates without a degree but with a compelling portfolio demonstrating scope and impact at staff/lead level will be considered.

2  Experience

• 6 – 8 years of professional software engineering experience in product-focused environments.


• Minimum 2 years in a formal or de-facto technical lead / staff engineer capacity across multiple squads or systems.


• Minimum 8-12 months of active, hands-on experience with AI coding tools in a professional engineering setting.


• Proven track record of shipping production systems with measurable business impact at scale.


• Demonstrated experience collaborating closely with ML/AI model teams on integration, deployment, and evaluation.

3  Technical Skills — Core Engineering

• Languages: Expert proficiency in at least two of — Python, TypeScript/JavaScript, Go.


• Architecture: Microservices, event-driven systems, API design (REST, GraphQL, gRPC), distributed systems fundamentals.


• Databases: SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis); vector databases (FAISS, Pinecone, Weaviate).


• Cloud: AWS, GCP, or Azure — compute, storage, serverless, managed ML services (SageMaker, Vertex AI).


• DevOps: Docker, Kubernetes, CI/CD (GitHub Actions, Jenkins); IaC (Terraform/Pulumi); observability stacks.


• Testing: TDD/BDD; unit, integration, and e2e frameworks; model evaluation pipelines.

4  AI / ML Integration Skills (Mandatory)

• Demonstrated proficiency with AI coding assistants (Cursor IDE, Claude Code, Codex CLI) in daily professional workflows.


• Experience designing and consuming LLM/SLM inference APIs; understanding of model serving, latency, and cost trade-offs.


• Hands-on familiarity with RAG architectures, vector stores, and retrieval pipelines.


• Ability to define and enforce AI code quality standards across an engineering team.


• Understanding of LLM limitations: hallucinations, context window constraints, prompt injection risks, and licensing considerations.

5  Technical Skills — Nice to Have

• Experience with SLM/LLM training or fine-tuning pipelines (SFT, RLHF, LoRA/QLoRA).


• Familiarity with agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) and multi-step reasoning pipelines.


• Contributions to open-source ML or developer tooling projects.


• Knowledge of OWASP Top 10, secure coding, and AI-specific security risks (prompt injection, model exfiltration).


• Prior experience in a high-growth startup or scale-up environment with rapid iteration cycles.

Behavioural Competencies

• AI-First Mindset: Defaults to AI tools to accelerate work while maintaining rigorous quality standards; actively pushes the team’s AI capability forward.


• Systems Thinking: Sees the full picture — how model outputs, product surfaces, and infrastructure interdepend; anticipates second-order effects of architectural choices.


• Ownership: Takes end-to-end responsibility from design through production; doesn’t hand off problems, solves them.


• Influence Without Authority: Earns trust through technical credibility; drives alignment through persuasion, data, and well-reasoned proposals.


• Communication: Writes clear design docs, ADRs, and post-mortems; articulates complex trade-offs for engineering peers and non-technical stakeholders.


• Mentorship: Actively invests in the growth of engineers at all levels; gives direct, constructive feedback.


• Pragmatism: Balances the ideal with the shipped; knows when to iterate vs. refactor vs. rewrite.


• Curiosity: Continuously explores emerging research, tools, and patterns — and brings those learnings back to the team.