ML Platform / Integration Engineer

Exasoft Pte. Ltd.; Singapore; On-site; SGD 90,000 - 140,000; Full time

Job summary

Exasoft Pte. Ltd. is seeking a software/platform engineer to develop and maintain Computer Vision services, APIs, and deployment tooling.

The role includes packaging ML models for controlled deployment, implementing health checks and robust logging, and coordinating with IT for infrastructure and security needs. You will build CI/CD pipelines, support staging and production deployments, and troubleshoot across application, ML model, and infra layers, delivering comprehensive runbooks and

Qualifications

• 3–8 years of relevant experience in software engineering, platform engineering, system integration or related areas.
• Diploma or Degree in Computer Science, Software Engineering, Information Technology, or a related field
• Strong hands-on Python development experience.
• Practical experience developing and integrating services and APIs.
• Experience with Linux, containers, version control, automated testing and CI/CD.
• Good understanding of monitoring, logging, databases, networking and production support.
• Strong troubleshooting skills across application and infrastructure environments.
• Good technical documentation and communication skills.
• Experience with Kubernetes and Microsoft Azure.
• Exposure to GPU workloads, video streaming or on-premises deployments.
• Experience with ML inference technologies such as ONNX, TensorRT or Triton.
• Previous experience supporting Computer Vision or production AI/ML workloads.

Responsibilities

• Develop and maintain Computer Vision services, APIs, configurations and error-handling mechanisms.
• Package ML services and models for controlled deployment, versioning and rollback.
• Build and maintain automated testing, CI/CD pipelines and release processes.
• Implement health checks, structured logging, monitoring and operational diagnostics.
• Work closely with IT teams on infrastructure, connectivity, certificates, access and environment requirements.
• Support staging and production deployments, including troubleshooting and incident investigation.
• Troubleshoot issues across application, ML model and infrastructure layers.
• Prepare deployment guides, technical documentation, runbooks and operational handover materials.
• Ensure solutions follow relevant security, privacy, access-control and change-management requirements.

Skills

Python development

Troubleshooting

Documentation

Communication

CI/CD

Monitoring

Networking

Education

Diploma or Degree in Computer Science/Software Engineering/Information Technology

Tools

Kubernetes

Microsoft Azure

Git

Docker

ONNX

TensorRT

Triton

Job description

Responsibilities

• Develop and maintain Computer Vision services, APIs, configurations and error-handling mechanisms.
• Package ML services and models for controlled deployment, versioning and rollback.
• Build and maintain automated testing, CI/CD pipelines and release processes.
• Implement health checks, structured logging, monitoring and operational diagnostics.
• Work closely with IT teams on infrastructure, connectivity, certificates, access and environment requirements.
• Support staging and production deployments, including troubleshooting and incident investigation.
• Troubleshoot issues across application, ML model and infrastructure layers.
• Prepare deployment guides, technical documentation, runbooks and operational handover materials.
• Ensure solutions follow relevant security, privacy, access-control and change-management requirements.

Requirements

• 3–8 years of relevant experience in software engineering, platform engineering, system integration or related areas.
• Diploma or Degree in Computer Science, Software Engineering, Information Technology, or a related field
• Strong hands-on Python development experience.
• Practical experience developing and integrating services and APIs.
• Experience with Linux, containers, version control, automated testing and CI/CD.
• Good understanding of monitoring, logging, databases, networking and production support.
• Strong troubleshooting skills across application and infrastructure environments.
• Good technical documentation and communication skills.
• Experience with Kubernetes and Microsoft Azure.
• Exposure to GPU workloads, video streaming or on-premises deployments.
• Experience with ML inference technologies such as ONNX, TensorRT or Triton.
• Previous experience supporting Computer Vision or production AI/ML workloads.