Accenture UK & Ireland
AI Infrastructure Architecture Manager
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Lead and Principal Infrastructure Architect (AI/ML)
About the Role
As a Lead and Principal Infrastructure Architect, you own the end-to-end responsibility for designing optimized compute infrastructure for large-scale AI and machine learning systems, including distributed training environments. You serve as the authority in translating business goals, SLAs, and client standards into infrastructure architectures that:
- Perform at scale
- Are deliberately engineered for cost-efficiency
- Ensure security, compliance, and regulatory adherence
Leveraging expertise in hyperscaler cloud platforms (AWS, Azure, or Google Cloud), you:
- Apply authoritative knowledge of AI/ML services, accelerators, and networking
- Drive best-in-class solutions for performance, scalability, and cost control
- Set technical direction, mentoring engineers while leading infrastructure roadmaps
- Collaborate with clients to shape enterprise and frontier-workload solutions
Responsibilities
- Lead architecture & design for AI/ML systems:
- Oversee end-to-end infrastructure for distributed training and deployment
- Evaluate and propose architecture alternatives across compute, networking, storage, orchestration, and model serving
- Make data-driven decisions tailored to client constraints, SLAs, and standards
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Architecture assessments & optimization:
- Identify gaps, risks, bottlenecks, and scalability opportunities
- Recommend remediation strategies
- Document technical rationale, trade-offs, and decisions for transparency
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Define and align infrastructure roadmaps:
- Plan technology evolution, capacity, and scaling for business/product goals
- Set cost-efficiency standards without sacrificing performance
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Expert cloud infrastructure leadership:
- Serve as the authority in one hyperscaler cloud platform (AWS, Azure, or GCP)
- Optimize large-scale GPU clusters, distributed training, and accelerators
- Ensure automation, CI/CD, and MLOps best practices
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Integrate AI systems into enterprise environments:
- Ensure interoperability, security, compliance, and regulatory adherence
- Align technical decisions with business outcomes
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Cost management & capacity planning:
- Lead forecasting, modeling, and cost optimization
- Define monitoring, observability, and performance tracking strategies
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Collaborate & lead cross-functional alignment:
- Engage with clients, stakeholders, and engineering teams
- Mentor engineers and architects
- Lead design reviews and establish technical standards
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Requirements
Education
- Bachelor’s Degree in:
- Computer Science
- Computer Engineering
- Related Engineering Field
Critical Experience & Skills
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AI/ML Infrastructure Expertise:
- Practical experience in coding, deploying, monitoring, and troubleshooting AI/ML models
- Knowledge of on-premise and hyperscaler cloud deployment architectures
- Strong background in AI/ML infrastructure (Compute, GPU clusters, networking, storage)
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Technical Proficiency: Python, Java, or C++ (deep language skills)
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Workflow & Pipeline Tools: Apache Airflow, Kubeflow
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Leadership & Project Management:
- Proven experience leading AI/ML teams in hyperscaler cloud environments
- Managing multi-project workloads with strong project management skills
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Cloud Platform Authority: Expertise in AWS, Azure, or Google Cloud (one preferred as primary)
- Deep knowledge of AI/ML services, accelerators, networking, and cost levers
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Soft Skills:
- Problem-solving in fast-paced environments
- Excellent communication & collaboration capabilities
- Experience translating requirements into scalable tech solutions
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