IT Infrastructure Engineer (2 Year Contract)

MORGAN MCKINLEY PTE. LTD.; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

MORGAN MCKINLEY PTE. LTD. is seeking an IT Infrastructure Engineer to manage multiple priorities in a fast-paced setting.

The role partners with Data Science and Product Management to productionise AI products on‑prem and on cloud AI platforms, while aligning with enterprise security standards and compliance. You will collaborate with Platform Architecture, Cybersecurity, and Data & Collaboration Platforms teams to design scalable, secure AI infrastructure and support ongoing platform operations

Qualifications

• Strong written and verbal communication skills to pitch ideas and influence stakeholders.
• Ability to assess project and compliance requirements and develop practical solutions.
• Hands-on experience building, securing and operating enterprise Data Science and AI platforms on AWS or on‑prem.

Responsibilities

• Deliver projects within the assigned practice to meet platform availability and resilience.
• Translate business requirements into platform infrastructure designs.
• Design, deploy and configure resilient, secure AI infrastructure with service providers.
• Ensure compliance with Enterprise Architecture and Security Standards and AI policies.
• Onboard hosting environments by designing, setting up, configuring and testing required infrastructure.
• Manage AI platform operations and address queries from Data Scientists and Product Managers.
• Improve runbooks, SOPs and automate to enhance AI platform experience.

Skills

Communication skills

AWS

DevSecOps

MLOps

LLMOps

IaC

Python

Containerization

Linux

PostgreSQL

Job description

Summary

As an IT Infrastructure Engineer, you will manage multiple priorities in a fast-paced environment while supporting stakeholder needs and responding to change. You are expected to partner with the Data Science and Product Management teams to productionise and host Data Science and AI products on-premises and cloud AI platforms. You will also work with the wider technologies team, including Platform Architecture and Engineering, Cybersecurity, and Data & Collaboration Platforms-to define requirements and integrate scalable, secure, and compliant AI platforms into the 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 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.

Requirements

• 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 working with Identity & Access Management services (e.g. Entra ID, AWS Cognito and AWS IAM), and Monitoring & Observability services (e.g. CloudWatch, Splunk and Elastic) are advantageous.


• Knowledge of PostgreSQL, vector databases, object storage, and the processing of structured and unstructured datasets is advantageous.


• Good understanding of AI, application and infrastructure security risks and controls.