Senior ICT Infrastructure Engineer

As a Senior Consultant ICT Infrastructure Engineer, you will manage multiple priorities in a fast-paced environment while supporting stakeholder needs and responding effectively to change. The role involves partnering with Data Science and Product Management teams to productionise and host Data Science and AI products on on-premises and cloud-based AI platforms, while collaborating with Platform Architecture and Engineering, Cybersecurity, and Data & Collaboration Platforms teams 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 enterprise architecture and security standards, as well as applicable technology 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 and Standard Operating Procedures (SOPs), and adhere to enterprise and compliance mandates
• Continually improve the AI platform experience by implementing new features and enhancements
• Continually simplify product development and operations through automation and the development of reusable services
• Assist in AI platform design reviews and the improvement of AI, operating system, 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 perspectives 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 is preferred
• Hands-on experience with Infrastructure-as-Code (IaC), automation, and Python technology stacks is preferred
• Experience working with Identity & Access Management services, such as Entra ID, AWS Cognito, and AWS IAM, and Monitoring & Observability services, such as CloudWatch, Splunk, and Elastic, is 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