Technology Manager
Posted 24 Aug 2026
Advertised as “Manager Technology (IT)”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 10+ yrs
- Management level
- People management
No salary listed for this role.
Field of studyComputer Science, Engineering, Information Technology (or related)
Study levelBachelor's
Study levelMaster's
Job Overview:
McDermott is seeking an experienced Technology Manager, Artificial Intelligence Infrastructure to lead the strategy, architecture, deployment, governance, and operations of enterprise AI infrastructure platforms and services. This role will be responsible for building and managing the foundational technologies that enable artificial intelligence, machine learning, generative AI, intelligent automation, and AI-driven operations across the organization.
The successful candidate will work closely with Infrastructure, Cloud, Security, Data & Analytics, Enterprise Architecture, and business stakeholders to establish a secure, scalable, resilient, and cost-effective AI ecosystem that supports current and future business objectives.
This position combines technical leadership, operational excellence, and strategic planning to accelerate McDermott's AI transformation journey.
Responsibilities
Key Tasks and Responsibilities:
AI Infrastructure Strategy & Architecture
- Develop and execute the enterprise AI infrastructure roadmap aligned with business and technology objectives.
- Design and oversee scalable AI platform architectures across on-premises, hybrid, and cloud environments.
- Establish standards, governance, and best practices for AI infrastructure services.
- Collaborate with Enterprise Architecture and Security teams to ensure compliance and alignment with corporate technology standards.
- Evaluate emerging AI technologies and identify opportunities for adoption.
AI Platform Engineering
- Lead implementation and operations of enterprise AI platforms, including:
- Azure AI Services
- Azure OpenAI Service
- Microsoft Copilot Technologies
- AI Agents and Agentic AI platforms
- Machine Learning platforms
- Vector databases and RAG architectures
- GPU and accelerated compute environments
- Design infrastructure supporting AI model training, inference, and deployment workloads.
- Establish AI workload lifecycle management practices.
Cloud & Infrastructure Management
- Manage AI infrastructure across Azure and hybrid environments.
- Oversee provisioning, capacity planning, performance optimization, and lifecycle management.
- Implement Infrastructure-as-Code (IaC) using Bicep, Terraform, and automation frameworks.