Machine Learning Engineer

Date: 21 Jul 2025

Location:

SG

Company: StarHub Ltd

Job Description

We are seeking a hands-on Machine Learning Engineer with deep experience in the AWS ecosystem to join our Data & AI team. You will be responsible for building, deploying, and scaling ML systems, working across the full model lifecycle—from experimentation and data processing to deployment, monitoring, and optimization.

The ideal candidate is an engineer who blends ML expertise with strong DevOps and software engineering practices, enabling automation, reproducibility, and continuous delivery of AI/ML solutions in a production environment.

Responsibilities

ML Pipeline Development
Design, build, and maintain scalable end-to-end ML pipelines using AWS services such as SageMaker, Glue, Lambda, Step Functions, S3, and EventBridge.

Model Lifecycle Management
Automate model training, hyperparameter tuning, deployment, and monitoring for production workloads using SageMaker Pipelines or custom workflows.

Model Development & Testing
Work with data scientists to operationalize models and implement automated unit, integration, and regression testing frameworks to validate performance and robustness before deployment.

Feature Engineering & Data Pipelines
Develop reusable and scalable feature pipelines in collaboration with data engineers, integrating with feature stores and real-time/batch data sources such as Kinesis, Redshift, and Glue.

Model Deployment & Monitoring
Deploy models for real-time and batch inference using SageMaker endpoints, Batch Transform, and monitor for latency, drift, and performance degradation using tools such as CloudWatch and Prometheus.

Continuous Integration & Testing
Integrate automated tests and validation checks into CICD pipelines using CodePipeline, GitHub Actions, or other orchestration tools to ensure quality and repeatability in model updates.

Configuration Management
Manage version control of models, datasets, infrastructure, and code using tools like Git, SageMaker Model Registry, and infrastructure-as-code frameworks such as CDK, Terraform, or CloudFormation.

Performance Optimization
Optimize model inference speed, resource usage, and deployment cost through profiling, batching, autoscaling, and fine-tuning.

Collaboration & Productionization
Work closely with data scientists to convert notebooks, prototypes, and POCs into robust, maintainable, and scalable production applications.

Documentation & Governance
Ensure thorough documentation, traceability, and adherence to AI governance, data privacy, and ML ethics standards throughout the development lifecycle.

Qualifications

Requirements

At least 5 years of experience as an ML Engineer, MLOps Engineer, or equivalent.
Strong proficiency in Python and ML frameworks such as Scikit-learn, XGBoost, PyTorch, or TensorFlow.
Proven experience with AWS ML services, including SageMaker, Glue, Lambda, Step Functions, S3, and CloudWatch.
Familiarity with CI/CD and test automation tools (e.g., PyTest, Unittest, GitHub Actions, CodePipeline).
Experience with infrastructure-as-code, versioning, and model registries.
Solid understanding of MLOps practices, testing strategies, and production ML requirements.
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Engineering, or related field.

Preferred Qualifications

Familiarity with Generative AI, LLM deployment, or RAG pipelines using AWS Bedrock, LangChain, or open-source LLMs.
Experience with vector databases (e.g., FAISS, OpenSearch), feature stores, and model explainability tools.
AWS certification (e.g., ML – Specialty, Solutions Architect, or DevOps Engineer) is a plus

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