ICT Infrastructure Engineer

TEAMLEASE DIGITAL SOLUTIONS PTE. LTD.; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

TEAMLEASE DIGITAL SOLUTIONS PTE. LTD. seeks an ICT Infrastructure Engineer in its Data Analytics & Engineering division to productionise AI products on PE-managed on‑premises and cloud platforms.

You will partner with Data Science, Product Management and Technology groups to design, deploy, and secure scalable AI infrastructure in Singapore. You will manage platform operations, improve runbooks, and support data scientists and product managers while ensuring regulatory and security compliance

Qualifications

• Hands-on experience building, securing and operating enterprise Data Science and AI platforms on AWS or on-premises environments.
• Experience with DevSecOps, MLOps and/or LLMOps pipelines.
• Familiarity with AI and container services, LLM inference and Linux.
• Knowledge of Infrastructure-as-Code, automation and Python tech stack.

Responsibilities

• Deliver projects to meet platform availability, reliability and resilience requirements.
• Translate business requirements into platform infrastructure designs.
• Design, deploy and configure resilient, secure AI infrastructure.
• Ensure compliance with security standards and policies.
• Onboard hosting environments by setting up and testing infrastructure.

Skills

AWS

Linux

Python

IaC

DevSecOps

MLOps

LLMOps

Containers

Data Science platforms

Tools

CloudWatch

Splunk

Elastic Stack

Entra ID

AWS Cognito

Kubernetes

Docker

Job description

As an ICT Infrastructure Engineer in Company’s Data Analytics & Engineering (DAE) Division, Production Engineering (PE) team, you will manage multiple priorities in a fast-paced environment while supporting stakeholder needs and responding to change. Specifically, you are expected to:

• Partner with DAE Data Science and Product Management teams to productionise and host Data Science and AI products on PE-managed on-premises and cloud AI platforms.
• Work with Technology Group divisions—including Platform Architecture and Engineering, Cybersecurity, and Data & Collaboration Platforms—to define requirements and integrate scalable, secure, and compliant AI platforms into the Company’s enterprise environment.

Key Responsibilities

• Deliver projects within the assigned practice to meet platform availability, reliability and resilience requirements.
• Translate business requirements into platform infrastructure designs.
• Work with service providers to design, deploy and configure resilient, available and secure AI infrastructure.
• Ensure compliance with the Authority’s Enterprise Architecture and Security Standards, as well as IM8 and AI Policies.
• Act as the point of contact for new AI product initiatives, supporting system design, integration, acceptance and performance testing.
• Manage AI platform operations and address queries from Data Scientists and Product Managers.
• Improve operational runbooks, SOPs and adhere to enterprise and compliance mandates.
• Continually improve AI platform experience by implementing new features and enhancements.
• Continually simplify product development and operations through automation and building reusable services.
• Assist in AI Platform design review and improvement of AI, OS, container and database security standards.
• Onboard hosting environments by designing, setting up, configuring, implementing, testing and commissioning the required infrastructure.
• Manage system architecture, resource planning, platform performance, and hosting across servers, operating systems, and containers, excluding hypervisor and storage layers.

What we are looking for

• Strong written and verbal communication skills, with the ability to pitch ideas, influence stakeholders and balance strategic perspective with business needs and challenges.
• Ability to assess project and compliance requirements and develop practical, sustainable solutions.
• Hands-on experience building, securing and operating enterprise Data Science and AI platforms on AWS or in on-premises environments.
• Hands-on experience building and operating DevSecOps, MLOps, and/or LLMOps pipelines.
• Hands-on experience with AI and container services, LLM inference and Linux preferred.
• Hands-on experience with Infrastructure-as-Code, automation and Python tech stack preferred.
• Experience with Identity & Access Management services (e.g. Entra ID, AWS Cognito and AWS IAM) and Monitoring & Observability services (e.g. CloudWatch, Splunk and Elastic) is advantageous.
• Knowledge of PostgreSQL, vector databases, object storage, and the processing of structured and unstructured datasets is advantageous.
• A good understanding of AI, application, and infrastructure security risks and controls.