Machine Learning Engineer

Role overview

We are looking for a seasoned Machine Learning Engineer to join a team in Singapore and help build advanced data and AI systems. This position is centered on designing, developing, and refining large-scale machine learning and data engineering solutions that support both streaming and batch workloads in enterprise settings.

The work will involve close partnership with Data Scientists, Data Engineers, and Platform teams to create resilient ingestion pipelines, productionize ML models, and deliver high-performance solutions that can work with structured, semi-structured, unstructured, and multi-modal data.

This is an onsite position based in Singapore, Singapore.

Key responsibilities

• Architect, build, and support highly scalable batch and real-time processing systems using Hadoop ecosystem tools such as Spark, Kafka, Flink, Hive, Iceberg, Trino, NiFi, Ranger, and Ozone.
• Develop reliable ingestion, transformation, and processing pipelines for data from structured, semi-structured, and multi-modal sources, including images, audio, video, and document-based content.
• Write scalable data pipelines with Java, Python, Spark, and shell scripting to support enterprise data and AI use cases.
• Work with Data Scientists to deploy, run, monitor, and maintain machine learning models in production.
• Use Cloudera Machine Learning and similar ML platforms to manage model deployment and lifecycle activities.
• Create data engineering solutions that can handle both streaming and large-volume batch processing needs.
• Build internal tools, automation utilities, and full-stack applications using Python and frameworks such as Flask and React.
• Tune performance, investigate issues, and troubleshoot Hadoop-based systems and distributed processing workloads.
• Track system health, resource use, and platform reliability, then implement improvements that raise efficiency and scalability.
• Maintain data governance, security, and access controls through tools such as Apache Ranger.
• Contribute to architecture reviews, technical design sessions, and modernization efforts across the platform.
• Support CI/CD, deployment automation, and operational best practices for ML and data engineering solutions.
• Keep up with new developments in machine learning, big data tools, and platform technologies.

Experience and qualifications

• A bachelor’s degree in Computer Science, Information Technology, Data Science, Engineering, or a closely related discipline is required.
• Applicants should bring at least 10 years of overall experience in software, data engineering, or platform engineering.
• A minimum of 6 years of direct experience in Machine Learning Engineering, Big Data Engineering, or Data Platform Development is required.
• Strong hands-on knowledge of Hadoop ecosystem technologies such as Apache Spark, Apache Hive, Apache Kafka, Apache Flink, Apache NiFi, and Apache Iceberg is expected.
• Solid programming skills in Java and/or Python are necessary.
• Proven experience building data ingestion, transformation, and processing frameworks is required.
• Hands-on exposure to both batch and real-time processing architectures is important.
• A strong grasp of distributed computing, scalable data platforms, and high-throughput data processing systems is needed.
• Experience deploying machine learning models with Cloudera Machine Learning, SparkMLlib, or comparable platforms is required.
• Familiarity with ML libraries such as scikit-learn, XGBoost, and similar Python-based frameworks is expected.
• Shell scripting and automation skills are required.
• Experience optimizing Hadoop-based applications and clusters for performance is important.
• Strong analytical thinking, problem-solving, and debugging abilities are needed.
• Good communication skills and the ability to collaborate with stakeholders are essential.
• Prior experience working in Agile environments is required.

Additional information

Work mode: Work from office.

Role type: Machine Learning Engineer.

Location: Singapore, Singapore.

Note: The role emphasizes large-scale enterprise data and AI workloads, operational excellence, and platform modernization.

Application note

The opportunity is intended for experienced professionals who can contribute immediately to advanced ML and data platform engineering initiatives.