ML Engineer — Production-Grade Models & Pipelines

INFOWIZ PTE LTD; Singapore; On-site; SGD 90,000 - 140,000; Full time

Job summary

The role covers end-to-end ML lifecycle, from data exploration to evaluation, deployment, and continuous optimisation in a fast-paced environment.

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 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.

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

ML concepts

Analytical thinking

Software engineering

CI/CD

Education

Bachelor's degree in CS/DS/Math/Engineering

Tools

Spark

PySpark

Flink

Job description

The role covers end-to-end ML lifecycle, from data exploration to evaluation, deployment, and continuous optimisation in a fast-paced environment.