Scale Engineer

Senior AI Engineer (Risk & Payments)

AI Engineer · Senior · Full Time

AU - SydneyUSD 140k – 200k1d ago
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Role

What you'll do.

Senior AI Engineer role at Airwallex focused on designing and deploying intelligent AI agent systems for risk management and payment processing. This position requires 2+ years of applied AI/ML engineering experience with production deployments, strong Python proficiency, and a proven track record building agentic frameworks and multi-step reasoning systems. You'll translate complex business problems into production-ready AI solutions using frameworks like LangChain, LlamaIndex, and AutoGen while balancing innovation with reliability and compliance.

Responsibilities

  • AI Agent Architecture and Deployment: Design, architect, and deploy AI agent systems using modern frameworks including LangChain, LlamaIndex, AutoGen, CrewAI, and Semantic Kernel. Optimize agent architectures for reasoning capabilities, tool integration, and end-to-end workflow automation in production environments.
  • Business Problem Translation: Translate complex business requirements from the Risk and Payments team into comprehensive AI agent solutions. Convert whiteboard concepts into production-ready systems that address concrete business challenges in payment processing and risk management.
  • Advanced AI Techniques Implementation: Apply the full spectrum of AI/ML techniques including prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and model selection. Make pragmatic decisions about technique application based on business requirements and performance constraints.
  • Production System Integration: Collaborate with backend and frontend engineers to seamlessly integrate AI reasoning capabilities into secure, scalable production systems. Ensure AI components work harmoniously with existing infrastructure while maintaining data security and compliance requirements.
  • Evaluation Framework and Monitoring Development: Build advanced evaluation frameworks and comprehensive monitoring systems for AI agents. Establish governance mechanisms to ensure responsible AI deployment, track model performance, and enable continuous improvement cycles.
  • Continuous Improvement and Iteration: Design systems that improve with usage, creating compounding AI flywheels. Monitor production performance, gather insights, and iteratively enhance agent reasoning, accuracy, and efficiency based on real-world usage patterns and business metrics.

Qualifications

What we look for.

Technical

  • Applied AI/ML Engineering

    Minimum 2+ years of hands-on experience in applied AI and machine learning engineering with demonstrated production deployments. Must have successfully taken AI/ML projects from conception through production, managing the complete lifecycle.

  • Python Programming

    Strong proficiency in Python programming with ability to write clean, maintainable, and efficient code. Experience building data pipelines, implementing algorithms, and optimizing performance-critical code.

  • ML Frameworks Expertise

    Demonstrated expertise with machine learning frameworks such as PyTorch, JAX, or TensorFlow. Ability to select appropriate frameworks based on use case requirements and implement complex models effectively.

  • AI Agent Development

    Proven track record building, deploying, and operating AI agents, orchestration frameworks, or multi-step reasoning systems. Experience with agentic AI systems, tool calling, and agent composition patterns.

  • Production AI Systems

    Experience with monitoring, evaluation, and iteration of AI systems in production environments. Understanding of performance metrics, failure modes, bias detection, and continuous model improvement strategies.

  • Prompt Engineering and RAG

    Practical experience with prompt engineering techniques, retrieval-augmented generation (RAG), and fine-tuning methodologies. Ability to optimize model behavior through prompting and specialized training approaches.

Education

  • Bachelor's Degree in Computer Science, Engineering, or Related Field

    Formal education in computer science, software engineering, mathematics, or a related discipline. This provides the foundational knowledge for AI/ML engineering roles.

  • Machine Learning Fundamentals

    Strong understanding of machine learning fundamentals including supervised/unsupervised learning, neural networks, optimization, and statistical concepts. Can be demonstrated through coursework, certifications, or practical project experience.

Experience

  • AI/ML Production Deployments

    2+ years of professional experience deploying AI and ML systems to production environments, managing end-to-end project delivery from ideation through deployment and maintenance.

  • Agentic AI Systems

    Track record of successfully building and deploying AI agents or multi-step reasoning systems that solve concrete business problems. Experience with workflow automation and complex reasoning architectures.

  • FinTech or Risk Domain Knowledge (Preferred)

    Experience in financial technology, payments, risk management, or fraud detection domains provides valuable context but is not strictly required. Domain expertise accelerates impact in this role.

  • Cross-Functional Collaboration

    Experience working with product, backend, frontend, and infrastructure teams to deliver integrated AI solutions. Ability to communicate technical concepts to non-technical stakeholders.

Skills

Required

  • Python

    Advanced Python programming for AI/ML applications, data processing, and system development.

  • PyTorch or JAX

    Deep learning framework expertise for model development, training, and deployment.

  • LLM and Agent Frameworks

    Hands-on experience with LangChain, LlamaIndex, AutoGen, CrewAI, or similar agentic frameworks.

  • Production ML Systems

    Experience building, monitoring, and maintaining ML systems in production environments with consideration for scalability, reliability, and governance.

  • Prompt Engineering

    Proficiency in crafting effective prompts and designing prompt chains for optimal model behavior and reasoning.

  • System Design

    Ability to design scalable, reliable systems that integrate AI components with backend infrastructure and databases.

  • Problem Solving

    Strong analytical and problem-solving skills with ability to decompose complex business challenges into actionable AI solutions.

Preferred

  • Retrieval-Augmented Generation (RAG)

    Nice to have

    Experience implementing RAG systems for improved model grounding and factuality in production environments.

  • Model Fine-tuning

    Nice to have

    Experience with fine-tuning large language models or other ML models for domain-specific applications.

  • Vector Databases

    Nice to have

    Familiarity with vector databases such as Pinecone, Weaviate, or Milvus for semantic search and RAG implementations.

  • Multi-agent Orchestration

    Nice to have

    Experience designing and implementing multi-agent systems with sophisticated orchestration and communication patterns.

  • FinTech Experience

    Nice to have

    Background in financial technology, payments systems, or risk/fraud detection domains.

  • ML Monitoring and Observability

    Nice to have

    Experience with ML monitoring platforms, A/B testing frameworks, and observability tools for production ML systems.

  • API Integration

    Nice to have

    Experience integrating with external APIs and third-party services, particularly in payment or financial domains.

  • Distributed Systems

    Nice to have

    Familiarity with distributed computing concepts, microservices architectures, and scalable system design.

Tech stack

Languages

Python

Frameworks

LangChainLlamaIndexAutoGenCrewAISemantic KernelPyTorchJAX

Databases

Vector DatabasesTraditional Relational Databases

Tools

ML Monitoring and EvaluationVersion ControlContainer Orchestration

Other

LLM Prompt EngineeringRetrieval-Augmented Generation (RAG)Model Fine-tuningAI Governance and ComplianceWorkflow Automation

Compensation

Pay and benefits.

Base·USD 140,000 – 200,000

Equity·Stock options

Benefits

  • Equity Compensation

    Stock options as part of compensation package, aligned with company success and growth trajectory.

  • Health and Wellness

    Comprehensive health insurance coverage including medical, dental, and vision plans with employer contributions.

  • Professional Development

    Learning and development budget for courses, certifications, conferences, and continuous skill advancement in AI/ML domains.

  • Global Work Environment

    Opportunity to work with a distributed, international team across 27 offices globally with exposure to diverse perspectives and markets.

  • Founder-like Ownership Culture

    Emphasis on ownership, impact, and autonomy in role execution with direct influence on product and strategic direction.

  • Collaborative Team Structure

    Cross-functional collaboration with experienced engineers, product managers, and business stakeholders on high-visibility problems.

  • Innovation and Impact

    Opportunity to work on cutting-edge AI agent systems at scale, influencing payment and risk infrastructure serving 250,000+ businesses.

Process

Interview steps.

  1. 01

    Application and Resume Review

    Initial screening of your application and resume against technical requirements, experience level, and relevant AI/ML background. Highlight specific projects involving AI agents, production deployments, and technical achievements.

  2. 02

    Technical Screening Call

    Conversation with a hiring team member to discuss your AI/ML engineering background, production deployment experience, and approach to agentic AI systems. Be prepared to discuss specific technical decisions and trade-offs in past projects.

  3. 03

    Technical Deep Dive Interview

    Detailed technical assessment covering AI architecture design, prompt engineering, RAG implementation, and system design for production AI systems. Expect questions on handling scaling challenges, model evaluation, and operational monitoring.

  4. 04

    Business Problem Case Study

    Work through a real or hypothetical business problem related to payment risk or financial operations. Demonstrate your ability to translate business requirements into AI agent architectures and propose implementation approaches.

  5. 05

    Collaboration and Communication Assessment

    Discussion with team members about how you approach cross-functional collaboration, communicate technical concepts to non-technical stakeholders, and work within fast-moving, iterative environments.

  6. 06

    Final Round Discussion

    Conversation with senior leadership covering your vision for AI-driven solutions in fintech, experience balancing innovation with reliability/compliance, and how you're motivated by the role and Airwallex's operating principles.

Full posting

Original listing.

About Airwallex

Airwallex is the only unified payments and financial platform for global businesses. Powered by our unique combination of proprietary infrastructure and software, we empower over 250,000 businesses worldwide – including Brex, Navan, Qantas, SHEIN and many more – with fully integrated solutions to manage everything from business accounts, payments, spend management and treasury, to embedded finance at a global scale.

Proudly founded in Melbourne, we have a team of over 2,300 of the brightest and most innovative people in tech across 27 offices around the globe. Valued at US$11 billion and backed by world-leading investors including T. Rowe Price, Visa, Mastercard, Robinhood Ventures, Sequoia, Salesforce Ventures, DST Global, and Lone Pine Capital, Airwallex is leading the charge in building the global payments and financial platform of the future. If you’re ready to do the most ambitious work of your career, join us.

 

Attributes We Value

We hire successful builders with founder-like energy who want real impact, accelerated learning, and true ownership. You bring strong role-related expertise and sharp thinking, and you’re motivated by our mission and operating principles. You move fast with good judgment, dig deep with curiosity, and make decisions from first principles, balancing speed and rigor.

You're humble and collaborative; turn zero‑to‑one ideas into real products, and you “get stuff done” end-to-end. You use AI to work smarter and solve problems faster. Here, you’ll tackle complex, high‑visibility problems with exceptional teammates and grow your career as we build the future of global banking. If that sounds like you, let’s build what’s next.

 

About the Role
You’ll be a core builder in our Risk and Payment team, designing and deploying intelligent systems that automate complex, multi-step workflows. You’ll leverage agentic frameworks, advanced orchestration, and pragmatic model selection to take ideas from whiteboard to production. This position will be embedded in the payment and risk team to build AI solutions improving risk management and payment processing.

What You’ll Do

  • Translate business problems into AI agent solutions end to end.

  • Architect and deploy AI agents using frameworks like LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, and emerging OSS stacks.

  • Apply the right techniques across the AI spectrum including prompting, RAG, fine-tuning.

  • Design and optimize agent architectures for reasoning, tool use, and workflow automation.

  • Build systems that improve with usage, creating compounding AI flywheels.

  • Work with backend and frontend engineers to integrate AI reasoning into secure, production systems.

  • Develop advanced evaluation framework and monitoring systems for AI agents to ensure good governance of AI

What You Need to Have

  • 2+ years in applied AI/ML engineering with production deployments.

  • Strong Python skills and experience with ML frameworks (PyTorch, JAX…).

  • Track record of building and deploying AI agents, orchestration frameworks, or multi-step reasoning systems for concrete business problems.

  • Understanding of monitoring, evaluation, and iteration in production AI systems.

  • Ability to balance innovation with reliability, scalability, and compliance.

Applicant Safety Policy: Fraud and Third-Party Recruiters

To protect you from recruitment scams, please be aware that Airwallex will not ask for bank details, sensitive ID numbers (i.e. passport), or any form of payment during the application or interview process. All official communication will come from an @airwallex.com email address. Please apply only through careers.airwallex.com or our official LinkedIn page.

Airwallex does not accept unsolicited resumes from search firms/recruiters. Airwallex will not pay any fees to search firms/recruiters if a candidate is submitted by a search firm/recruiter unless an agreement has been entered into with respect to specific open position(s). Search firms/recruiters submitting resumes to Airwallex on an unsolicited basis shall be deemed to accept this condition, regardless of any other provision to the contrary.

Equal opportunity

Airwallex is proud to be an equal opportunity employer. We value diversity and anyone seeking employment at Airwallex is considered based on merit, qualifications, competence and talent. We don’t regard color, religion, race, national origin, sexual orientation, ancestry, citizenship, sex, marital or family status, disability, gender, or any other legally protected status when making our hiring decisions. If you have a disability or special need that requires accommodation, please let us know.

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