# Senior AI Engineer (Risk & Payments)
**Company:** [Airwallex](https://scaleengineer.com/companies/airwallex)
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 with production deployments, strong Python expertise, and proven experience building agentic frameworks and orchestration systems. You'll translate complex business problems into end-to-end AI solutions while working across risk, payments, backend, and frontend teams in a fast-paced fintech environment.
**Role:** ML Engineer
**Seniority:** Senior
**Locations:** SG - Singapore
**Salary:** 120000–180000 USD
[Apply](https://jobs.ashbyhq.com/airwallex/c7070452-6ec6-443c-8015-db339403634a)
Canonical: https://scaleengineer.com/jobs/airwallex/senior-ai-engineer-risk-payments-c7070452
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## Responsibilities

- AI Agent Architecture & Development: Design, architect, and deploy production-grade AI agent systems using industry-leading frameworks including LangChain, LlamaIndex, AutoGen, CrewAI, and Semantic Kernel. Translate complex business requirements into multi-step reasoning workflows that automate risk assessment and payment processing operations.
- End-to-End AI Solution Development: Own the complete lifecycle of AI solutions from conceptualization to production deployment, including business problem analysis, technical feasibility assessment, implementation, testing, and monitoring. Ensure solutions balance innovation with reliability, scalability, and regulatory compliance requirements.
- Advanced Model Application & Optimization: Apply sophisticated AI techniques across the spectrum including prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and agentic orchestration. Optimize agent architectures for complex reasoning, dynamic tool use, and workflow automation specific to financial risk and payments domains.
- Evaluation Framework & Governance Development: Develop comprehensive evaluation frameworks and monitoring systems for AI agents to ensure robust governance, performance tracking, and compliance in production environments. Implement observability and feedback mechanisms that enable continuous improvement and risk mitigation.
- Cross-Functional Integration & Collaboration: Partner with backend engineers, frontend engineers, product managers, and risk specialists to seamlessly integrate AI reasoning capabilities into secure, scalable production systems. Communicate technical capabilities and limitations clearly to non-technical stakeholders.
- AI Flywheel Development: Design systems that improve iteratively with real-world usage, creating compounding AI flywheels that enhance risk management accuracy and payment processing efficiency. Implement feedback loops that leverage production data to continuously refine model performance.

## Requirements

### education

- {"name":"Degree in Computer Science, Mathematics, Physics, or Related Field","description":"Bachelor's degree (or equivalent professional experience demonstrating equivalent mastery) in Computer Science, Mathematics, Physics, Engineering, or closely related quantitative discipline."}
- {"name":"Advanced Coursework in Machine Learning","description":"Formal education or self-directed mastery of machine learning fundamentals including supervised learning, deep learning, probabilistic methods, and optimization. Can be demonstrated through coursework, certifications, or substantial personal projects."}

### technical

- {"name":"Applied AI/ML Engineering Production Experience","description":"Minimum 2+ years of hands-on applied AI/ML engineering with demonstrated track record of building, deploying, and maintaining production systems at scale. Experience should include end-to-end ownership from model development through production monitoring and optimization."}
- {"name":"Python Programming Expertise","description":"Advanced proficiency in Python for ML systems development, data processing, and AI application engineering. Demonstrated ability to write clean, efficient, production-grade Python code with strong software engineering practices."}
- {"name":"ML Frameworks & Deep Learning","description":"Strong hands-on experience with modern ML frameworks including PyTorch, JAX, or TensorFlow. Ability to leverage these frameworks for model development, fine-tuning, and optimization in production environments."}
- {"name":"AI Agent & Agentic Frameworks","description":"Proven experience architecting and deploying AI agents and multi-step reasoning systems using frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, or Semantic Kernel. Track record of translating complex workflows into agent-based solutions."}
- {"name":"Production AI System Design","description":"Demonstrated expertise in designing and implementing monitoring, evaluation, and iteration systems for production AI. Understanding of production deployment challenges, performance optimization, failure modes, and governance requirements."}
- {"name":"LLM & Advanced ML Techniques","description":"Working knowledge of large language models, prompt engineering, retrieval-augmented generation (RAG), fine-tuning approaches, and other contemporary AI techniques. Ability to select and apply the right technique for specific business problems."}

### experience

- {"name":"AI Agent System Deployment","description":"Hands-on experience building, testing, and deploying AI agent systems or complex orchestration frameworks that solve concrete business problems. Track record should demonstrate progression from prototype to production-scale implementations."}
- {"name":"Reasoning & Workflow Automation Systems","description":"Experience designing multi-step reasoning systems and intelligent workflow automation that handle complex, sequential decision-making. Prior work with tool use, function calling, or action planning in agentic contexts is valuable."}
- {"name":"Financial Services or High-Compliance Domains","description":"Background working on systems requiring high reliability, security, and compliance standards is beneficial. Experience with risk systems, fraud detection, payment processing, or regulated financial services is a strong advantage."}
- {"name":"Cross-Functional Technical Leadership","description":"Experience collaborating with backend, frontend, and product teams to integrate AI solutions into production systems. Track record of communicating technical AI concepts to diverse stakeholder groups and driving consensus on technical decisions."}

## Skills

### required

- {"name":"Python","description":"Production-grade Python development for ML systems, data pipelines, and application integration. Must demonstrate ability to write scalable, maintainable code."}
- {"name":"PyTorch or JAX","description":"Deep hands-on experience with at least one modern deep learning framework for model development, training, and optimization."}
- {"name":"AI Agents & Orchestration Frameworks","description":"Proven expertise with LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, or similar agentic frameworks for building multi-step reasoning systems."}
- {"name":"Large Language Models (LLMs)","description":"Working knowledge of LLM capabilities, limitations, prompt engineering, and integration patterns. Experience prompting and fine-tuning models for specific tasks."}
- {"name":"Production ML Systems","description":"Experience designing, deploying, and maintaining production ML systems including monitoring, evaluation, A/B testing, and performance optimization."}
- {"name":"System Design & Architecture","description":"Ability to design scalable system architectures that integrate AI reasoning with backend systems, considering reliability, latency, and compliance requirements."}
- {"name":"Problem Solving & First Principles Thinking","description":"Strong analytical and problem-solving skills with ability to break down complex business problems and design pragmatic technical solutions balancing innovation with reliability."}

### preferred

- {"name":"Retrieval-Augmented Generation (RAG)","description":"Experience implementing RAG systems for knowledge-intensive tasks, vector databases, and context retrieval optimization."}
- {"name":"Fine-tuning & Model Adaptation","description":"Hands-on experience fine-tuning LLMs or foundation models for domain-specific tasks, including LoRA, QLoRA, and other efficient adaptation techniques."}
- {"name":"Evaluation & Benchmarking Frameworks","description":"Experience designing and implementing evaluation frameworks, metrics, and benchmarking approaches for assessing AI system performance in production."}
- {"name":"Data Engineering & ETL","description":"Familiarity with data pipelines, ETL processes, and data infrastructure supporting ML systems. Knowledge of SQL and data warehousing concepts."}
- {"name":"API Integration & Backend Development","description":"Experience integrating ML systems with production APIs, REST services, and microservice architectures. Familiarity with containerization and deployment tools."}
- {"name":"Risk & Fraud Detection Systems","description":"Prior experience building or working on risk assessment, fraud detection, or financial services systems. Understanding of risk modeling and compliance requirements."}
- {"name":"Emerging AI Frameworks & Techniques","description":"Curiosity and hands-on experience experimenting with cutting-edge AI frameworks, prompt optimization techniques, and novel approaches to agentic systems."}
- {"name":"Cloud Infrastructure & DevOps","description":"Proficiency with cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes), and CI/CD practices supporting ML deployment."}

## Tech stack

### tools

- {"name":"Git & Version Control","description":"Essential for collaborative development, code management, and CI/CD workflows in production environments."}
- {"name":"Docker & Containerization","description":"Container technology for packaging, deploying, and scaling AI applications in cloud and on-premise environments."}
- {"name":"Jupyter Notebooks","description":"Development environment for experimentation, prototyping, and iterative AI system development."}
- {"name":"Model Serving & Inference Platforms","description":"Tools for deploying models to production, managing inference, and handling model versioning. Examples include TorchServe, Triton, or cloud-native solutions."}
- {"name":"Monitoring & Observability Tools","description":"APM and logging solutions for tracking AI system performance, debugging issues, and ensuring governance in production."}

### others

- {"name":"Prompt Engineering & Optimization","description":"Techniques for designing effective prompts, few-shot learning, chain-of-thought reasoning, and dynamic prompt generation for LLMs."}
- {"name":"Retrieval-Augmented Generation (RAG)","description":"Architecture pattern combining information retrieval with generative models to improve accuracy and reduce hallucination in AI systems."}
- {"name":"Fine-tuning Techniques","description":"Methods for adapting foundation models to domain-specific tasks including full fine-tuning, LoRA, QLoRA, and other parameter-efficient approaches."}
- {"name":"Evaluation Frameworks & Metrics","description":"Approaches for assessing AI agent performance including business metrics, hallucination detection, reasoning validation, and compliance checking."}
- {"name":"Function Calling & Tool Use","description":"Patterns for enabling AI agents to interact with external tools, APIs, and systems to complete multi-step workflows and access real-time data."}

### databases

- {"name":"Vector Databases","description":"Specialized databases for storing and querying embeddings, essential for RAG systems and semantic search in AI agents."}
- {"name":"PostgreSQL","description":"Relational database commonly used for storing structured data, application state, and integration with Python-based systems."}
- {"name":"Redis","description":"In-memory data store for caching, session management, and high-performance data access in production systems."}

### languages

- {"name":"Python","description":"Primary language for AI/ML development, data processing, and system integration. Essential for all aspects of this role."}

### frameworks

- {"name":"LangChain","description":"Leading framework for building AI agent applications with chaining, memory management, and integration with LLMs and external tools."}
- {"name":"LlamaIndex","description":"Data framework for building context-augmented AI applications with efficient indexing and retrieval capabilities for RAG systems."}
- {"name":"AutoGen","description":"Multi-agent framework by Microsoft for building conversational AI systems with agent orchestration and autonomous workflows."}
- {"name":"CrewAI","description":"Framework for orchestrating role-based AI agents working collaboratively on complex tasks with specialized capabilities."}
- {"name":"Semantic Kernel","description":"Microsoft's SDK for integrating LLM capabilities into applications with plugins, planners, and orchestration features."}
- {"name":"PyTorch","description":"Deep learning framework for model development, training, fine-tuning, and deployment of neural networks."}
- {"name":"JAX","description":"Composable transformation framework for high-performance numerical computing and machine learning research."}

## Benefits

### benefits

## Compensation

- **max:** 180000
- **min:** 120000
- **currency:** USD
- **stockOptions:** true

## Interview process

### steps

- {"name":"Initial Screening & Background Review","description":"HR team reviews application materials focusing on AI/ML production experience, framework expertise, and alignment with Airwallex's mission and values."}
- {"name":"Technical Phone Screening","description":"Hiring manager or senior engineer conducts phone conversation to assess depth of ML/AI knowledge, production deployment experience, and technical communication skills. Expect discussion of past projects and specific agentic frameworks."}
- {"name":"AI System Design Challenge","description":"Technical assessment requiring design and implementation of an AI agent system or agentic workflow. May involve LLM integration, RAG implementation, or multi-step reasoning. Typically done asynchronously or in-person depending on location."}
- {"name":"Production Systems Deep Dive","description":"Technical interview exploring production ML system design, monitoring, evaluation frameworks, and operational challenges. Candidates should be prepared to discuss trade-offs between innovation and reliability, deployment strategies, and governance."}
- {"name":"Risk & Payments Domain Knowledge Discussion","description":"Interview with risk or payments domain experts to assess understanding of use cases, ability to learn domain context, and potential impact on risk management and payment processing workflows."}
- {"name":"Cross-Functional Collaboration Interview","description":"Meeting with backend and frontend engineering leads to assess collaboration skills, communication ability with non-ML engineers, and systems thinking."}
- {"name":"Leadership & Values Alignment Interview","description":"Senior leadership discussion covering career trajectory, motivation, alignment with Airwallex operating principles (first principles thinking, founder-like energy, getting stuff done), and long-term career goals."}
- {"name":"Offer Discussion & Compensation Negotiation","description":"Final stage involving offer presentation, discussion of compensation package including base salary, equity, benefits, and role expectations."}

## Full description
## **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](https://www.airwallex.com/us/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_](http://airwallex.com) _email address. Please apply only through_ [_careers.airwallex.com_](http://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.