Accenture
AI Infrastructure Lead Architect
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Lead and Principal Infrastructure Architect (AI/ML Specialist)
About the Role
You will own end-to-end responsibility for designing optimized compute infrastructure for large-scale AI and machine learning systems, including distributed training environments. As the authority, you translate business goals, SLAs, and client standards into infrastructure architectures that perform at scale while ensuring cost-efficiency. Your deep technical expertise allows you to weigh viable solutions across compute, networking, storage, orchestration, and model serving, making well-justified architectural decisions tailored to each client’s constraints, standards, and requirements.
Key responsibilities include:
- Architecting and optimizing the full computational stack for performance, power, cost, and scalability.
- Designing and tuning large-scale GPU clusters and distributed training systems.
- Ensuring solutions meet security, compliance, and regulatory requirements.
- Applying authoritative knowledge of at least one hyperscaler cloud (AWS, Azure, or Google Cloud), focusing on AI/ML services, accelerators, networking, and cost optimization.
Beyond technical design, you will lead technical direction, mentor engineers, and partner with clients to shape the infrastructure roadmap while ensuring solutions align with business SLAs, enterprise-scale workloads, and cost-efficiency.
Key Responsibilities
Architecture & Design
- Own the end-to-end architecture of optimized compute infrastructure for AI/ML systems.
- Develop and evaluate architecture alternatives, balancing trade-offs in compute, networking, storage, orchestration, and model serving.
- Lead architecture assessments, identifying gaps, risks, bottlenecks, and optimization opportunities.
- Document rationale, trade-offs, and assumptions for transparent and defensible decisions.
System Optimization & Strategy
- Define and maintain the AI infrastructure roadmap, aligning with business and product goals.
- Architect large-scale GPU clusters and distributed training environments, optimizing for performance and cost-efficiency.
- Design automation and CI/CD workflows for reliable, scalable deployments of AI systems and models.
- Establish AI monitoring and observability strategies, including SLAs, performance tracking, cost optimization, and scaling.
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Integration & Compliance
- Integrate AI/ML systems into enterprise environments, ensuring security, interoperability, and compliance.
- Lead capacity planning and cost modeling, forecasting needs and engineering cost-efficiency.
Leadership & Collaboration
- Set technical direction, standards, and best practices for AI infrastructure.
- Mentor engineers and architects, review designs, and drive modeling, training, and troubleshooting.
- Collaborate with clients and stakeholders to align infrastructure decisions with business outcomes.
Platform Expertise
- Serve as the authoritative AI infrastructure voice in hyperscaler clouds (AWS, Azure, or GCP).
- Leverage expertise in AI/ML services, accelerators, networking, and cost optimization.
About Accenture
Accenture is a leading global professional services company, concentrating on building digital foundations, operational efficiency, innovation, and citizen engagement for businesses and governments.
With approximately 791,000 employees serving clients in 120+ countries, we emphasize innovation, agility, and sustainability, delivering impact through expertise in cloud computing, data, AI, and industry innovation.
Commitments:
- Equality & Inclusion: We foster a diverse, collaborative, and inclusive environment where every individual is valued and empowered.
- Customer-Centric Values: Our mission is to drive tangible value for our clients, team members, partners, and society while embracing global collaboration.
- Innovation: We invest in technology and upskilling to ensure our team thrives and drives meaningful change.
Culture & Growth:
- Support employees at every stage of their career journey.
- Fusion of diverse backgrounds and cutting-edge tools to deliver scalable success.
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For more information, visit www.accenture.com.
Equal Employment Opportunity Statement:
We believe in a world where differences are celebrated, and no one is discriminated against based on:
- Age, race, color, religion, sex, sexual orientation, gender identity, disability, national origin, ancestry, marital status, citizenship status, or any other legally protected classification.
Education & Qualifications
Required:
- Bachelor’s degree in Computer Science, Computer Engineering, or a related engineering/technology field.
Basic (Required) Qualifications:
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Technical Depth:
- Strong background in coding, building, monitoring, and troubleshooting AI/ML applications.
- Expertise in selecting, designing, and deploying AI infrastructure on premises or public clouds.
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AI/ML Knowledge:
- Deep understanding of AI and machine learning frameworks, services, and applications.
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Infrastructure Expertise:
- Strong grasp of computing infrastructure, particularly AI infrastructure (preferred).
- Hands-on experience with configured computing environments for GPU workloads.
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Technical Skills:
- Proficiency in Python, Java, or C++.
- Experience with data pipeline and workflow tools (e.g., Apache Airflow, Kubeflow).
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Problem-Solving & Leadership:
- Strong problem-solving skills for fast-paced environments.
- Experience managing AI/ML infrastructure projects on hyperscaler platforms.
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Collaboration & Stakeholder Engagement:
- Ability to communicate technically with both technical and non-technical stakeholders.
- Experience in cross-functional collaboration, from users and clients to AI engineering teams.
Additional Notes:
- Requires hands-on experience leading AI/ML infrastructure on large-scale platforms.
- Demonstrated ability to guide AI initiatives, mentor junior team members, and align technology with business objectives.
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