Senior Software 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 hiring Senior Engineers who combine solid software engineering fundamentals with proven proficiency in AI-assisted development. This role is designed for engineers who have actively used AI coding tools (such as Cursor, Claude Code, Codex CLI only) for at least one year and can leverage these capabilities to ship higher-quality code faster. You will be embedded in product engineering squads, leading feature development, mentoring junior engineers, and championing AI-first development practices across the team.

What Makes This Role Different

This is not an AI research role - it is a core engineering role for engineers who use AI as a force multiplier. We expect you to:

• Use AI coding assistants daily to accelerate development without sacrificing code quality.


• Critically evaluate AI-generated code for correctness, security vulnerabilities, and maintainability.


• Share best practices for AI-assisted workflows with the broader engineering team.


• Help define team standards for responsible and effective AI tool adoption.

Key Responsibilities

1  Feature Engineering & Delivery

• Design, build, and ship production-quality features across the full stack or within your domain (backend, frontend, platform, or data).


• Leverage AI coding assistants to write, refactor, document, and test end-to-end code efficiently.


• Translate product requirements and technical specifications into clean, maintainable implementations.


• Lead feature squads for medium-complexity initiatives from design through deployment.


• Participate in and drive sprint planning, backlog grooming, and technical estimation sessions.

2  AI-Augmented Development Practices

• Champion the use of AI pair-programming tools (Copilot, Cursor, Claude, etc.) across the engineering org.


• Develop and document internal guidelines for effective AI prompt engineering within coding workflows.


• Conduct training sessions or knowledge-share presentations on maximising productivity with AI tools.


• Critically audit AI-generated code for logic errors, security issues, and performance anti-patterns before merging.


• Stay updated on the latest AI development tooling and evaluate new tools for team adoption.

3  Code Quality & Engineering Excellence

• Write testable, modular, and well-documented code following the team's style guides and design principles.


• Drive test-driven development (TDD) and maintain code coverage targets.


• Perform thorough code reviews with constructive feedback; uphold engineering standards.


• Identify and address technical debt proactively; contribute to architecture improvements.


• Participate in RCA (root cause analysis) for production incidents; implement lasting fixes.

4  Architecture & Technical Design

• Contribute to system design discussions, proposing solutions with appropriate trade-off analysis.


• Create and maintain technical design documents (TDDs), ADRs (Architecture Decision Records), and API contracts.


• Evaluate and adopt relevant third-party libraries, APIs, and SaaS tools with a security-first mindset.


• Collaborate with platform / DevOps teams on observability, CI/CD pipelines, and infrastructure-as-code.

5  Mentoring & Team Growth

• Mentor 1–2 mid-level engineers; conduct regular 1:1 technical coaching sessions.


• Onboard new team members by sharing codebase context, tooling setup, and team practices.


• Contribute to a culture of psychological safety, learning from failures, and continuous improvement.

6  Cross-functional Collaboration

• Work closely with Product Managers, Designers, and Operations to deliver delightful, high-quality user experiences.


• Coordinate with data and AI/ML teams to integrate model endpoints, APIs, and AI features into product surfaces.


• Participate in customer-facing escalations where deep technical expertise is required.

Required Qualifications

1  Education

• Bachelor's degree in Computer Science, Software Engineering, or a related field.


• Candidates without a degree but with a strong portfolio and demonstrable project history will be considered.

2  Experience

• 3 – 5 years of professional software engineering experience in a product-focused environment.


• Minimum 1 year of active, hands-on experience using AI coding tools in professional work (not just personal projects).


• Track record of shipping production features end-to-end with measurable business impact.

3  Technical Skills — Core Engineering

• Languages: Proficiency in at least one of — Python, TypeScript/JavaScript, Java, Go, or Kotlin.


• Web / API: REST, GraphQL, gRPC; familiarity with microservices and event-driven architectures.


• Databases: SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis, DynamoDB).


• Cloud: AWS, GCP, or Azure — compute, storage, serverless, and managed services.


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


• Testing: Unit, integration, and e2e testing frameworks; TDD/BDD familiarity.


• Version Control: Git, branching strategies (GitFlow, trunk-based), PR workflows.

4  AI Tooling Skills (Mandatory)

• Demonstrated proficiency with at least one AI coding assistant (Cursor IDE, Claude Code, Codex CLI).


• Ability to craft effective prompts for code generation, refactoring, debugging, and documentation tasks.


• Experience reviewing and validating AI-generated code for correctness and security.


• Understanding of limitations of LLM-based coding tools (hallucinations, context window, license risks).

5  Nice to Have

• Contributions to open-source projects.


• Experience building or integrating AI/ML features (e.g., calling LLM APIs, embedding models, RAG pipelines).


• Familiarity with agentic coding frameworks (LangChain, LlamaIndex, AutoGen, CrewAI).


• Experience in a startup or scale-up environment with rapid iteration cycles.


• Knowledge of web security (OWASP Top 10, secure coding practices).

Behavioural Competencies

• AI-First Mindset: Naturally reaches for AI tools to accelerate tasks while maintaining engineering rigour.


• Ownership: Takes end-to-end accountability — from design to deployment to monitoring.


• Pragmatism: Balances perfect with shipped; knows when to iterate vs. over-engineer.


• Mentorship: Invests in the growth of peers and junior colleagues.


• Communication: Writes clearly (code comments, docs, Slack) and articulates trade-offs confidently.


• Curiosity: Continuously explores new tools, languages, and architectural patterns.


• Collaboration: Works well in cross-functional squads; builds trust quickly.