Enterprise AI Engineer: Open-Source Quickstarts & Blueprints

Red Hat; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Red Hat’s Singapore AI COE seeks an AI/ML Engineer to design production-grade architecture blueprints and deliver end-to-end AI quickstarts for enterprise clients. You will collaborate with partners to ensure secure, scalable deployments across the stack, from data ingestion to model serving.

You will work on cutting-edge ML frameworks, deploy on Kubernetes, and contribute to open-source efforts while advancing architectural depth and autonomous delivery in Ambiguous environments.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or related quantitative field.
• Strong software engineering foundations: clean code, test-driven development (TDD), CI/CD automation pipelines, and version control (Git).
• Clear verbal and written communication in English.

Responsibilities

• Design and build production-ready architecture blueprints and reference codebases with enterprise-grade requirements.
• Package repeatable AI Quickstarts to solve real-world problems.
• Collaborate with external engineering teams to validate joint blueprints and integrations.
• Design automated tests, evaluation harnesses, and telemetry to measure performance.
• Incorporate robust data privacy and secure design into all blueprints.

Skills

Python (ML)

Go

C/C++

PyTorch

Hugging Face

Kubernetes

CI/CD

Git

TDD

English communication

Education

Bachelor’s or Master’s in CS/CE

Tools

Ray

MLflow

Kubeflow

Job description

Red Hat’s Singapore AI COE seeks an AI/ML Engineer to design production-grade architecture blueprints and deliver end-to-end AI quickstarts for enterprise clients. You will collaborate with partners to ensure secure, scalable deployments across the stack, from data ingestion to model serving.

You will work on cutting-edge ML frameworks, deploy on Kubernetes, and contribute to open-source efforts while advancing architectural depth and autonomous delivery in Ambiguous environments.