Machine Learning Engineer

Changi Airport Group; Singapore; On-site; SGD 120,000 - 190,000; Full time

Job summary

Changi Airport Group is seeking an hands-on ML/AI Engineer to design, build, and deploy machine learning models into production. You will collaborate with cross-functional teams to translate business requirements into AI solutions.

The role covers modern AI/ML lifecycles, MLOps practices, and working across computer vision, NLP, and generative AI to ensure data governance and production reliability.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
• At least 3 years of experience in machine learning engineering, AI engineering, or related roles.
• Strong programming and software engineering skills.
• Hands-on experience with machine learning and modern AI/agentic frameworks (e.g., PyTorch, scikit-learn, LangChain, or similar).
• Understanding of the end-to-end machine learning lifecycle, including data preparation, model development, evaluation, deployment, and monitoring.
• Understanding of software engineering best practices (testing, version control, CI/CD).
• Familiarity with a range of machine learning techniques across domains such as computer vision, NLP, and/or generative AI.
• Experience with data processing tools and large-scale data systems.
• Experience building and deploying machine learning models in production environments.
• Experience deploying applications using APIs, containers, and orchestration tools.
• Familiarity with cloud platforms (AWS, Azure, or GCP).

Responsibilities

• Design, develop, and deploy machine learning models and AI systems into production
• Build and maintain scalable ML pipelines covering data ingestion, training, evaluation, deployment, and monitoring
• Collaborate with cross-functional teams to translate business requirements into AI/ML solutions
• Optimise models and systems for performance, scalability, and reliability in production environments
• Implement MLOps best practices including CI/CD, model versioning, experiment tracking, and automated retraining
• Monitor and maintain model performance, including handling drift and system reliability
• Develop and integrate AI capabilities across domains such as computer vision, natural language processing, and modern approaches including generative AI or agent-based systems where applicable
• Ensure adherence to data governance, security, and best engineering practices

Skills

ML engineering

Python programming

Software engineering

Cloud familiarity

Education

Bachelor's or Master's in CS/Engineering/Data Science

Tools

PyTorch

scikit-learn

LangChain

Job description

Key Responsibilities

• Design, develop, and deploy machine learning models and AI systems into production


• Build and maintain scalable ML pipelines covering data ingestion, training, evaluation, deployment, and monitoring


• Collaborate with cross-functional teams to translate business requirements into AI/ML solutions


• Optimise models and systems for performance, scalability, and reliability in production environments


• Implement MLOps best practices including CI/CD, model versioning, experiment tracking, and automated retraining


• Monitor and maintain model performance, including handling drift and system reliability


• Develop and integrate AI capabilities across domains such as computer vision, natural language processing, and modern approaches including generative AI or agent-based systems where applicable


• Ensure adherence to data governance, security, and best engineering practices

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field


• At least 3 years of experience in machine learning engineering, AI engineering, or related roles


• Strong programming and software engineering skills


• Hands-on experience with machine learning and modern AI/agentic frameworks (e.g., PyTorch, scikit-learn, LangChain, or similar)


• Understanding of the end-to-end machine learning lifecycle, including data preparation, model development, evaluation, deployment, and monitoring


• Understanding of software engineering best practices (testing, version control, CI/CD)


• Familiarity with a range of machine learning techniques across domains such as computer vision, natural language processing, and/or generative AI


• Experience with data processing tools and large-scale data systems


• Experience building and deploying machine learning models in production environments


• Experience deploying applications using APIs, containers, and orchestration tools


• Familiarity with cloud platforms (AWS, Azure, or GCP)