AI Infrastructure Architect

YOU ARE   As a hands- on  Infrastructure   Architect, you are an experienced engineer with several years in infrastructure engineering who now takes on more complex, higher-impact work designing and   optimizing   the AI and machine learning infrastructure that powers real-world applications. Working alongside senior architects and engineers — and increasingly leading your own workstreams — you apply proven skills in coding, testing, configuring, deploying,   monitoring , and troubleshooting AI systems and the infrastructure they run on. Day to day, you architect and optimize infrastructure components, write and review code and deployment scripts, design and tune cloud and on-premises compute resources such as GPU clusters and distributed training environments, deploy AI systems and models into production, and build and optimize data pipelines that feed AI and ML workflows. You   optimize   the computational stack for performance, cost, power, and scalability,   monitor   AI systems and infrastructure health across both   InfraOps   and   MLOps   disciplines, perform AI monitoring to track model and system performance, and independently troubleshoot and resolve complex issues across the stack. You also mentor junior engineers, contribute to architectural decisions, and help   establish   best practices. This is a hands-on, ownership-driven role where you apply and deepen your expertise across modern tools and platforms — including container orchestration, model serving, CI/CD pipelines,   InfraOps ,   MLOps , and AI monitoring — while making meaningful contributions to infrastructure that enables AI-driven business outcomes.   THE WORK   Write, review, and debug code, scripts, and infrastructure-as-code for AI infrastructure, automation, and tooling, setting standards for quality across the team.   Architect, configure, and provision compute resources across cloud and on-premises environments, including GPU clusters and distributed training setups,   optimizing for   performance and   utilization .   Design and   maintain   deployment automation and CI/CD pipelines to support reliable, repeatable releases of AI systems, models, and applications.   Deploy AI systems, models, and data pipelines into production, defining and improving the processes and best practices others follow.   Lead container orchestration and model serving using tools such as Docker, Kubernetes, and model deployment frameworks.   Architect and   optimize   the computational stack for performance, power, cost, and scalability, balancing trade-offs against business goals.   Evaluate and select tools, frameworks, and platforms, making recommendations that shape the infrastructure roadmap.   Integrate AI models and systems into existing enterprise systems, ensuring interoperability, security, and regulatory compliance.   Own AI monitoring and infrastructure health across   InfraOps   and   MLOps , tracking performance, reliability, and   utilization , and driving remediation.   Independently troubleshoot and resolve complex issues across the computational stack — hardware, networking, software, and models — and lead root-cause analysis.   Mentor junior engineers and lead code reviews, providing technical direction and supporting their growth.   Define and document architecture standards, processes, and procedures, and apply security, cost-efficiency, and   scalability   best practices across the infrastructure.   EDUCATION   • Bachelor's Degree in Computer   Science,  Computer   Engineering, related Engineering field   BASIC (REQUIRED) QUALIFICATION   Practical experience in coding, building, monitoring,   troubleshooting  applications   of AI/ML models; selecting, designing and infrastructure   for  deploying   and running them   on  premise   or on public cloud.   Strong understanding of AI and machine learning as a subject.   Strong understanding of computing   infrastructure  a   subject, preferred knowledge of AI infrastructure.   Proficiency   in programming languages such as Python, Java, or C++.   Experience with data pipeline and workflow management tools (e.g., Apache Airflow, Kubeflow).   Strong problem-solving skills and ability to work in a fast-paced environment.   Excellent communication and collaboration skills.   Proven experience in AI/ML infrastructure engineering or related roles on a   hyperscaler   platform for deploying large scale solutions.   Compensation at Accenture varies depending on a wide array of factors including but not limited to role, level, seniority, responsibility, skillset, and level of experience. For this position, the Metalmeccanico National Collective Bargaining Agreement applies, with one of the following pay grades and corresponding gross annual salary range: B2 - €32,000 - €50,500 B3 - €36,000 - €65,800 About Accenture Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our