Software Engineer II, Machine Learning Platform

AIRWALLEX (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

A leading fintech company in Singapore is looking for an engineer to design and optimize a machine learning platform for risk management. The role involves building scalable data pipelines and collaborating with teams to enhance risk strategies. Candidates should have extensive software engineering experience and a strong background in big data technologies. This position offers an innovative work environment focused on security and collaboration.

Qualifications

• More than 3 years in software engineering, with focus on machine learning platforms.
• Experience with big data technologies like Apache Spark, Hadoop.
• Proficient in at least one data engineering programming language.

Responsibilities

• Design and maintain scalable data pipelines for ML model training.
• Develop feature generation systems to ensure data quality for models.
• Implement MLOps best practices for continuous integration and deployment.

Skills

Software engineering experience

Machine learning platforms

Big data technologies

Data engineering

ETL/ELT pipelines

Real-time data streaming

Feature generation and feature stores

Containerization

Education

Bachelor's or Master's in Computer Science or Engineering

Tools

Apache Spark

Hadoop

Apache Kafka

Docker

Kubernetes

Job description

About the team

The Risk Platform Team at Airwallex is responsible for managing the risk for all the products at Airwallex, including GTPN, PA, Issuing, Onboarding, and Account takeover. The risk landscape is constantly changing, and fraudsters are becoming increasingly sophisticated. We are at the forefront of innovation in risk management. Our team builds and maintains the robust, scalable infrastructure that powers our advanced machine learning models, enabling rapid iteration and deployment of risk strategies.

What you’ll do

Our mission is to keep Airwallex's products and services safe and secure, and make Airwallex a trusted partner for businesses around the world. You will be instrumental in designing, building, and optimizing the end-to-end machine learning platform that enables rapid development, deployment, and monitoring of ML models. We leverage cutting-edge technologies, including big data frameworks, real-time streaming, MLOps best practices, graph technologies, and Large Language Models (LLMs), to implement and improve our strategy.

Our team expands across Beijing, Shanghai and Singapore. We collaborate with other teams (Data Science, Product, Engineering) and our customers globally to ensure a holistic approach for risk management and deliver state-of-the-art ML capabilities.

Responsibilities:

1. Design, build, and maintain scalable and reliable data pipelines for ingesting, processing, and transforming large datasets (batch and stream) for ML model training and inference.
2. Develop and manage feature generation systems and feature stores, ensuring data quality, consistency, reusability, and accessibility for model development.
3. Architect and implement robust model serving infrastructure for deploying, managing, and monitoring machine learning models in production at scale, ensuring low latency and high availability.
4. Collaborate with Data Scientists, ML Engineers, and Senior Technical Staff to understand ML requirements and translate them into robust platform capabilities and infrastructure.
5. Champion and implement MLOps best practices, including CI/CD for ML, model versioning, experiment tracking, automated monitoring, and feedback loops.
6. Experiment with new technologies and frameworks in the big data, streaming, and MLOps space, proposing architectural improvements for the ML platform.
7. Support and mentor less-experienced team members in ML platform engineering, big data technologies, data pipeline development, and MLOps practices.
8. Ensure the security, scalability, performance, and cost-effectiveness of the ML platform components.

Who you are We're looking for people who meet the minimum qualifications for this role. The preferred qualifications are great to have, but are not mandatory.

Minimum qualifications:

1. More than 3 years of software engineering experience, with at least 1+ years focused on building and maintaining machine learning platforms, big data systems, or large-scale data infrastructure.
2. Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
3. Proven experience with big data technologies (e.g., Apache Spark, Hadoop, Presto, Hive, Flink) and distributed computing.
4. Strong proficiency in at least one programming language commonly used in data engineering and ML (e.g., Python, Scala, Java).
5. Experience in designing, implementing, and managing complex ETL/ELT data pipelines and workflow management tools (e.g., Apache Airflow, Kubeflow Pipelines, Dagster).
6. Hands-on experience with real-time data streaming technologies (e.g., Apache Kafka, Flink, Spark Streaming) and building stream processing applications.
7. Experience with designing and implementing solutions for feature generation, feature engineering at scale, and/or feature stores.
8. Familiarity with model serving patterns, tools, and infrastructure (e.g., KFServing, Seldon Core, BentoML, Triton Inference Server, or custom API development for model deployment).
9. Experience with containerization (e.g., Docker) and orchestration (e.g., Kubernetes).
10. Proficient with build tools (e.g., Gradle, Maven, SBT) and version control systems (e.g., Git).

Preferred qualifications:

1. In-depth knowledge of MLOps principles and hands-on experience implementing MLOps pipelines and tools for continuous integration, continuous delivery, and continuous training (CI/CD/CT).
2. Experience with cloud platforms (e.g., Google Cloud, AWS, Azure) and their ML/data services (e.g., Vertex AI, SageMaker, Databricks, EMR).
3. Familiarity with various database technologies (e.g., NoSQL, SQL, Graph Databases) and their application in ML systems.
4. Knowledge of infrastructure-as-code tools (e.g., Terraform, Ansible).
5. Understanding of machine learning algorithms, model lifecycle management, and evaluation metrics.
6. Experience building platforms for risk management, fraud detection, or anomaly detection, particularly within financial and fintech industries.
7. Strong analytical and problem-solving skills, with the ability to tackle complex technical challenges.
8. Excellent communication and collaboration skills, 1 with experience working in agile environments.