ML Engineer

NTT Ltd; Singapore; On-site; SGD 80,000 - 110,000; Full time

Job summary

A leading technology services provider in Singapore is seeking a Machine Learning Engineer specializing in AWS workflows and ML operations. The ideal candidate will have strong expertise in designing, building, and automating ML workflows, ensuring robust CI/CD practices. Responsibilities include operationalizing ML solutions and managing high-quality production pipelines. Proficient knowledge in AWS services like SageMaker and practical experience with Airflow for workflow orchestration is crucial. Competitive compensation offered.

Qualifications

• Demonstrable hands-on AWS experience is essential.
• Experience with SageMaker Pipelines for model training and deployment.
• Expertise in CI/CD practices for ML workflows.

Responsibilities

• Design and deploy end-to-end ML workflows on AWS.
• Automate ML operations and manage ML pipelines effectively.
• Ensure high-quality production-grade ML pipelines.

Skills

AWS experience

SageMaker Pipelines

Automating ML operations

CI/CD practices

Python

PySpark

SQL

GitLab

Airflow

Tools

AWS services

Airflow

Job description

Overview

Machine Learning Engineer – AWS Workflow Specialist / ML Ops

We are looking for a Machine Learning Engineer with strong expertise in designing, building, automating, and deploying end‑to‑end ML workflows on AWS. The ideal candidate will have deep experience in operationalising ML solutions, implementing robust CI/CD practices, and ensuring high-quality, production-grade ML pipelines.

Key Skills and Experience

• Only CVs clearly demonstrating hands-on AWS experience will be considered
• Proven experience in designing, developing, and deploying SageMaker Pipelines, including data preparation, model training, evaluation, and model registry integration
• Ability to operationalise ML pipelines through the full development lifecycle — development, testing, integration testing, CI/CD, and production deployment
• Experience writing and maintaining unit tests, integration tests, and pipeline validation tests for ML workflows and SageMaker components
• Strong experience automating ML operations using Airflow DAGs, including dependency management, scheduling, error handling, and operational monitoring
• Ability to develop unit tests for Airflow DAGs and validate DAG logic as part of CI/CD workflows
• Extensive experience with GitLab (or similar) in enterprise environments, covering repository management and governance, branching strategies, merge request workflows, and CI/CD pipeline configuration for ML and data workflows
• Strong understanding of code management practices, versioning, environment isolation, and artifact management
• Proficient in Python, PySpark, and SQL for developing robust ML pipelines
• Hands-on experience with AWS services including S3, KMS, Lambda, Secrets Manager, CodeBuild, CodePipeline, SageMaker Pipelines, and SageMaker Endpoints
• Experience managing secure, scalable cloud environments following enterprise security standards
• Hands-on experience in orchestrating complex workflows using Airflow and integrating real-time streaming data from Kafka