Machine Learning Engineer

INFOWIZ PTE LTD; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

INFOWIZ PTE LTD in Singapore seeks a data-centric ML engineer to build and productionise predictive models, recommendations, and intelligent systems that drive product performance and user engagement. You will work across the ML lifecycle—from data exploration and feature engineering to model development, evaluation, deployment, and continuous optimisation together with data scientists and engineers.

Applicants should have strong Python skills, experience with Spark/Flink, and a track record of

Qualifications

• Bachelor's degree or above in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field.
• 3+ years of relevant experience in building recommendation systems, ranking models, personalisation, or user behaviour modelling.
• Strong programming skills in Python and good software engineering fundamentals.
• Strong understanding of machine learning algorithms, statistics, model evaluation, and feature engineering.
• Experience with large-scale datasets using Spark, PySpark, Flink or other distributed processing technologies.
• Hands-on experience building or deploying ML models in production environments.
• Strong analytical and problem-solving skills, with the ability to translate business problems into data and machine learning solutions.

Responsibilities

• Develop, productionise and maintain machine learning models for recommendation, personalisation, user behaviour, prediction, classification and other data-driven applications.
• Build scalable ML pipelines covering data preparation, feature engineering, model training, evaluation, deployment and monitoring.
• Design and optimise batch and/or real-time model inference solutions for reliability, scalability, latency and production performance.
• Work with large volumes of structured and unstructured data to develop effective machine learning solutions.
• Collaborate closely with Data Scientists and Data Engineers to transform ML prototypes and data pipelines into reliable production systems.
• Monitor model and system performance, identify degradation or operational issues, and continuously improve deployed solutions.
• Contribute to ML engineering practices, including testing, versioning, CI/CD, reproducibility and model lifecycle management.
• Evaluate new developments in machine learning, MLOps and AI and apply relevant technologies to business and product use cases.

Skills

Python

Software engineering

Machine learning

Statistics

Model evaluation

Feature engineering

Production ML

Education

Bachelor's degree or above in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field

Tools

Spark

PySpark

Flink

Job description

This role sits at the intersection of machine learning, big data engineering, and data science. You will work with large-scale datasets to develop predictive models, recommendation solutions, and intelligent systems that support product performance, user engagement, and business decision-making. You will be involved throughout the ML lifecycle—from data exploration and feature engineering to model development, evaluation, deployment, and continuous optimisation.

Key Responsibilities

• Develop, productionise and maintain machine learning models for recommendation, personalisation, user behaviour, prediction, classification and other data-driven applications.
• Build scalable ML pipelines covering data preparation, feature engineering, model training, evaluation, deployment and monitoring.
• Design and optimise batch and/or real-time model inference solutions for reliability, scalability, latency and production performance.
• Work with large volumes of structured and unstructured data to develop effective machine learning solutions.
• Collaborate closely with Data Scientists and Data Engineers to transform ML prototypes and data pipelines into reliable production systems.
• Monitor model and system performance, identify degradation or operational issues, and continuously improve deployed solutions.
• Contribute to ML engineering practices, including testing, versioning, CI/CD, reproducibility and model lifecycle management.
• Evaluate new developments in machine learning, MLOps and AI and apply relevant technologies to business and product use cases.

Requirements

• Bachelor's degree or above in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field.
• 3+ years of relevant experience in building recommendation systems, ranking models, personalisation, or user behaviour modelling.
• Strong programming skills in Python and good software engineering fundamentals.
• Strong understanding of machine learning algorithms, statistics, model evaluation, and feature engineering.
• Experience working with large-scale datasets using Spark, PySpark, Flink or other distributed processing technologies.
• Hands‑on experience building or deploying ML models in production environments.
• Strong analytical and problem-solving skills, with the ability to translate business problems into data and machine learning solutions.

Nice to Have

• Experience in gaming, e-commerce, fintech, advertising, or other data-intensive industries.
• Familiarity with cloud platforms such as AWS, Azure, or GCP is an advantage.