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 150,000 businesses worldwide – including Brex, Rippling, 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 1,700 of the brightest and most innovative people in tech across 26 offices around the globe. Valued at US$6.2 billion and backed by world-leading investors including Visa, Airtree, Blackbird, Sequoia, DST Global, Greenoaks, Salesforce Ventures, Lone Pine, and Square Peg, 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.

Airwallex is a rapidly growing fintech unicorn, founded in 2015 and now flourishing at the Series E financing stage.  With a global footprint across Asia-Pacific, Europe, North America, and APAC, we specialize in providing diverse financial payment solutions for B2B merchants, empowering businesses to operate seamlessly anywhere, anytime.

Core Responsibilities

As a Senior Software Engineer, you will be responsible for designing and building the core services that power our platform. You will tackle complex technical challenges, ensuring our systems remain highly available, scalable, and efficient as we continue to grow globally.

• Architecting Scalable Systems: Design and implement robust, high-performance backend services capable of handling high-volume traffic and complex data workflows.

• Optimizing Distributed Infrastructure: Build and maintain reliable internal and external-facing systems, focusing on low latency and high availability.

• Enhancing Data Retrieval: Develop and optimize search and data platforms to ensure fast and accurate information retrieval across distributed datasets.

• Driving Engineering Excellence: Lead technical initiatives, perform deep-dive code reviews, and mentor junior engineers to maintain high standards of code quality and system design.

• Cross-Functional Collaboration: Partner with Product, DevOps, and Security teams to translate business requirements into scalable technical solutions.

What You’ll Do

• Solve High-Scale Challenges: Address intricate, organization-wide problems by leveraging modern technologies in distributed systems, reactive programming, and workflow orchestration.

• Build Mission-Critical Infrastructure: Develop shared libraries and frameworks that eliminate technical bottlenecks and accelerate the pace of innovation across the entire engineering organization.

• Technical Strategy: Conduct evaluations of emerging technologies and guide long-term architectural decisions to ensure global resilience and efficiency.

Minimum Qualifications

• Technical Expert: You have strong proficiency in backend programming languages such as Java, Go, Rust, or Python and their associated ecosystems.

• Distributed Systems Specialist: You have a deep understanding of microservices architecture, including service discovery, load balancing, and inter-service communication.

• Database & Storage Expert: You possess hands-on experience with relational and non-relational databases (e.g., PostgreSQL, Redis, Elasticsearch) and understand data consistency and performance tuning at scale.

• Cloud & Infrastructure Literate: You are comfortable working with cloud providers (AWS, GCP, or Azure) and container orchestration tools.

• API Specialist: You have extensive experience in API design and lifecycle management, with a firm grasp of security protocols (OAuth, JWT, mTLS).

• Clear Communicator: You can articulate complex technical concepts to both technical and non-technical stakeholders in a multicultural environment.

Good to have

• Performance Engineering: Experience with advanced profiling (e.g., flame graphs) and system-level optimization.
• Observability & Reliability: Familiarity with monitoring, tracing, and logging tools (Prometheus, Jaeger, ELK) to maintain system health.
• Open Source Contributor: You actively follow or contribute to the open-source community and enjoy bringing cutting-edge industry trends into your work.
• Familiarity with Kubernetes and Docker.

JOB OVERVIEW:

The Data Scientist (AI Engineer) serves as the critical technical bridge between high-level business strategy and scalable AI production. This role is dedicated to accelerating demand creation by translating complex client challenges into feasible, high-impact AI/ML prototypes and Proof of Concepts (PoCs).

By balancing technical rigor with strategic communication, the incumbent assesses data maturity, designs agile solutions using both traditional and Generative AI, and ensures that all innovations are architecturally sound and ready for production handover. Ultimately, this role drives organizational growth by de-risking AI investments and demonstrating the tangible technical value of emerging technologies.

Core Focus Areas

• Technical Translation: Converting business "pain points" into rigorous technical requirements and feasible AI architectures.

• Rapid Innovation: Designing and iterating on interactive prototypes that showcase the "art of the possible" while adhering to vetted Technology Readiness Level (TRL) standards.

• Strategic Advisory: Evaluating client data infrastructure and providing technical roadmaps to bridge the gap between current capabilities and AI readiness.

• Seamless Integration: Partnering with Production and Enablement leads to ensure prototypes are scalable, documented, and transitioned without technical debt.

• Technology Scouting: Researching and applying cutting-edge tools from the internal Tech Radar to solve real-world client problems.

DUTIES AND RESPONSIBILITIES:

Technical Requirements Elicitation & Analysis:

• Collaborate with AI/ML Business Analysts and clients to deeply understand their existing technical landscape, data infrastructure, and specific operational challenges.

• Translate high-level business needs into technical requirements for AI/ML solutions, identifying necessary data inputs, system integrations, and potential technical constraints.

• Assess the technical feasibility of proposed AI/ML use cases, ensuring alignment with the organization’s vetted tech stack and TRL standards, given the client's current data capabilities and technology stack.

AI/ML Solution Conceptualization & Prototyping:

• Design and develop rapid, interactive prototypes or proof-of-concepts (PoCs) using appropriate AI/ML techniques (both traditional ML and cutting-edge AI like Generative AI).

• Demonstrate the functionality and potential business impact of AI/ML solutions through compelling technical demonstrations and visualizations.

• Iterate rapidly on prototypes based on client feedback, showcasing agility and responsiveness.

Technical Communication & Value Articulation:

• Simplify complex AI/ML concepts, algorithms, and technical architectures into understandable terms for non-technical business stakeholders.

• Clearly articulate the technical value proposition of proposed solutions, explaining how the AI/ML will work and why it is the right approach for the client's problem.

• Present technical findings, prototype results, and architectural considerations to clients and internal teams.

Organizational Data Capability Assessment (Technical):

• Conduct technical assessments of client data maturity, including data quality, accessibility, governance, and existing infrastructure.

• Advise clients on technical prerequisites and potential roadmaps for enhancing their data capabilities to support AI/ML initiatives.

Emerging Technology Research & Application:

• Actively research and evaluate new AI/ML technologies, tools, and frameworks (e.g., specific algorithms, cloud AI services, MLOps tools) relevant to demand creation and prototyping.

• Support the AI Solution Enablement Lead by exploring specific application-layer use cases for technologies already on the internal Tech Radar. Provide field feedback on the performance of vetted tools in client prototypes

• Identify how emerging technical capabilities can be applied to solve client problems and create new service offerings.

• Contribute technical insights to market exploration and strategic planning.

Cross-Functional Collaboration & Handover:

• Work in close partnership with the Data Science / AI Production Team from the earliest stages to ensure that prototyped solutions are designed with production scalability and maintainability in mind.

• Collaborate primarily with the AI Solution Enablement Lead to transition validated TRL 3 prototypes into the hardening phase. Ensure all initial technical elicitation is documented according to Enablement Quality Gate standards to prevent technical debt during handover

• Facilitate smooth technical handovers of validated concepts, prototypes, and initial architectural considerations to the production team, minimizing rework and ensuring continuity.

• Provide technical context and support during the transition phase to ensure the production team has a comprehensive understanding of the solution's intent and design.

• Problem Solving & Innovation:

• Apply strong analytical and problem-solving skills to technical challenges encountered during the demand creation and prototyping phases.

• Propose innovative technical approaches to address client needs and differentiate our solutions.

QUALIFICATIONS AND EXPERIENCES:

• Minimum Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field. Master's or Ph.D. preferred.

• At least 5+ years of progressive experience in AI/ML solution design, data science, or AI engineering, with a significant portion in a client-facing or consulting capacity.

• Proven experience in designing, developing, and deploying functional AI/ML prototypes that demonstrate business value and are engineered for production.

• Expert proficiency in Python and extensive experience with core AI/ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).

• Demonstrated hands-on experience with Generative AI and Large Language Models (LLMs), including frameworks like LangChain, for designing and prototyping novel applications.

• Strong practical experience with Google Cloud Platform (GCP) services relevant to AI/ML (e.g., Vertex AI, BigQuery, Cloud Storage, Dataflow, Cloud Functions).

• Proficiency with Docker for containerization and a foundational understanding of container orchestration (e.g., Kubernetes concepts for GKE).

• Solid understanding of MLOps principles for reproducibility, experiment tracking (e.g., MLflow, Vertex AI ML Metadata/Model Registry), and model versioning.

• High-level expertise in data governance frameworks, data quality assessment (including tools/libraries like Great Expectations), metadata management (e.g., Google Cloud Data Catalog), and data architecture principles.

• Strong expertise in distributed computing and parallel processing techniques is vital for handling large-scale AI workloads. 

• A proven track record of innovations and executions in deep learning, demonstrated through shipping products or first-author publications at leading AI conferences, is a strong differentiator.

• Experience in the container shipping industry or related logistics/supply chain domains is highly advantageous.

• Excellent presentation, documentation, and stakeholder management skills.

• Experience Requirements:
• Must have QA/Testing Experience.
• Data Testing Experience specifically in Big Data, Hadoop, or Cloud Data Warehouse environments.
• Databricks Experience of testing pipelines within a Databricks environment.
• Automation Focus: Proven track record of moving from manual SQL checks to automated Python-based testing frameworks.
• Mandatory: Databricks Certified Data Engineer Associate (at minimum).
• Preferred: ISTQB Foundation or Advanced Level (Test Automation Engineer).
• Core Technical Skills:
• Data Validation & Frameworks
• Great Expectations / Pandera: Proficiency in using Python-based libraries to define data "contracts" and automated validation suites.
• DLT Expectations: Deep understanding of Delta Live Tables (DLT) expectations (Fail, Drop, Quarantining bad records).
• Advanced SQL: Expert-level SQL for complex data reconciliation, identifying duplicates, and null-value analysis across billions of records.
• Python for QA (PySpark):
• Pytest-Spark: Experience using pytest to write unit tests for PySpark transformations and logic.
• Notebook Testing: Ability to write automated test notebooks that validate Medallion Architecture transitions (Bronze to Silver, Silver to Gold).
• Data Reconciliation: Building Python scripts to perform "source-to-target" counts and checksums across distributed file systems.
• Performance & Integration Testing:
• Scalability Testing: Ability to validate that data pipelines meet performance SLAs when data volume spikes.
• End-to-End Orchestration Testing: Testing the reliability of Databricks Workflows and handling of job failures/retries.
• Schema Evolution: Testing how pipelines handle upstream schema changes without breaking downstream Gold tables.
• Governance & Security Testing:
• Unity Catalog Validation: Testing Row-Level Security (RLS) and Column-Level Masking to ensure unauthorized users cannot see sensitive data.
• Data Lineage: Validating that data lineage in Unity Catalog correctly reflects the movement of data across the Lakehouse.

Job Description & Requirements

We are seeking a motivated and technically skilled Wire Cut / EDM Application Engineer to join our team. This role blends customer-facing support with technical expertise, ensuring optimal machine performance while contributing to product innovation. The ideal candidate thrives on hands-on engineering, enjoys problem-solving, and values close collaboration with both customers and product teams.

Key Responsibilities:

• Conduct pre-sales test cuts to demonstrate machine performance and capabilities.

• Provide post-sales support, including troubleshooting and technical assistance.

• Deliver training sessions to new users to ensure effective machine operation.

• Collaborate with product development teams by sharing customer and market feedback to guide new product design.

• Travel to regional countries as needed to support customers, deliver training, and conduct demonstrations.

Requirements:

• Minimum Diploma / Degree in Engineering (Mechanical, Manufacturing, or a related field).

• Strong interest in hands-on work with machinery

• Proficiency in SolidWorks, Microsoft Office

• Excellent critical thinking and problem-solving skills

• Strong communication skills for customer interaction and training

• Willingness to travel regionally when required.

• Prior experience as a machine user (Wire Cut/EDM) is an advantage.

• Background in application engineering or technical support roles is beneficial

Responsibilities

• Responsible for the design, programming, debugging, testing, modifications, deployment, operation, and maintenance of the applications.

• Responsible for developing digital products or features to support operations.

• Ensure that the developed modules/deliverables meet expectation, quality standards and target schedule.

• Effectively document issues and provide regular status and progress updates towards project milestones.

• Maintain and update the user guide to reflect system enhancements.

Requirements

• At least 1 year of hands-on working experience with Node.js, ReactJS, TypeScript, Shell Scripting, MySQL, NoSQL, Object-Relational Mapping (ORM) or other similar tech stack

• Bachelor’s Degree, Post Graduate Diploma or Professional Degree in Engineering (Computer/Telecommunication), Computer Science/Information Technology or an equivalent.

• Able to understand and take over existing implementations effectively.

• Able to document project know-how regularly and share with the team (system documentation, incident reporting, etc.).

• Experienced in internet technologies, application servers, and databases.

• Familiar with Git, Design Patterns, and Unit Testing.

• Experience with repositories and CI/CD pipelines such as GitLab, Bamboo and BitBucket

• Experience developing in Mac/Linux environment.

• Experience with AWS (Certification preferred), including foundational services like EC2, S3, and Amazon RDS.

• Strong verbal and written communication skills, able to transmit information in a clear way.

• Problem-solving skills with a high level of initiative.

• A team-player who takes ownership and is highly collaborative.

• Ability to work under pressure and meet tight deadlines.

• A quick learner who can work with minimum supervision.

Good-to-haves

• Experience with Amazon Connect, Cloudwatch, Code Pipeline, Lambda, CloudFront, Amazon SES are a plus.

• Familiarity with a serverless setup is a plus.

• Experience and certifications in cloud infrastructure deployment (Azure or Google Cloud).

• Working experience in Agile and UX development environment is an added advantage.

JOB SUMMARY:

We're looking for a Data Engineer with strong software engineering fundamentals to build and scale our data infrastructure. You'll design high-performance systems that handle massive data volumes and high-concurrency workloads, optimize query engines for speed and reliability, and architect robust data pipelines. This role is for engineers who love solving distributed systems challenges and building production-grade infrastructure that processes terabytes of data daily.

This role is open to fresh graduates and early-career professionals (1–2 years of experience) who are eager to grow their skills in building scalable data systems.

Whether you’re just starting out or already have some experience, this is a great opportunity to develop your data engineering skills and contribute to impactful, data-driven decision-making.

KEY RESPONSIBILITIES:

• Design, develop and maintain data infrastructures, such as computation, query engine and services which can support massive data and high concurrency requests
• Clean, transform, and optimize raw data for storage and analysis
• Work with structured and unstructured data from various sources (databases, APIs, files, etc.)
• Plan data warehouse structure to ensure data quality, accuracy, consistency, and availability
• Optimise for the performance of the data pipeline and service to downstream
• Collaborate with business analysts, data scientists, and backend teams to provide appropriate tools
• Document data models, processes, and technical decisions

What You'll Learn:

• Real-world data engineering with modern tools like Apache Spark/Flink, Kafka, Airflow
• Working with SQL/NoSQL databases, data lakes, and cloud platforms (AWS, GCP, Azure)
• Building batch and streaming data pipelines
• Data modeling, warehousing
• Orchestration and monitoring of data workflows
• Developing high availability services
• Best practices in data governance, privacy, and security
• Collaboration in agile, cross-functional teams with product, engineering, and analytics

JOB REQUIREMENTS: