AI Infrastructure Architect - Infrastructure & Capital Projects Engineering (All Genders)

Accenture DACH; Kronberg im Taunus; On-site; EUR 120,000 - 190,000; Full time

Job summary

Accenture DACH is seeking an experienced AI Infrastructure Architect to design and optimize AI/ML infrastructure powering real-world applications. You will architect compute resources across cloud and on-prem, build pipelines, and lead deployment of AI systems.

The role combines hands-on engineering with architectural leadership, mentoring junior staff and defining best practices across InfraOps and MLOps in a dynamic, fast-paced environment.

Qualifications

• Bachelor's Degree in Computer Science, Computer Engineering or related field.
• Proven experience in coding, building, monitoring and troubleshooting AI/ML infrastructure.
• Strong understanding of AI/ML and computing infrastructure, with hyperscaler experience preferred.
• Proficiency in Python, Java, or C++.
• Experience with data pipelines and workflow tools (e.g., Airflow, Kubeflow).
• Excellent communication and collaboration skills; able to mentor juniors.

Responsibilities

• Write, review, and debug code and infrastructure-as-code for AI infra and tooling.
• Architect and provision compute resources across cloud and on-prem environments.
• Design and maintain CI/CD pipelines for AI systems and deployments.
• Deploy AI systems and data pipelines into production with best practices.
• Lead container orchestration and model serving (Docker, Kubernetes).
• Optimize stack for performance, cost, and scalability and drive improvements.
• Evaluate tools and platforms to shape the infra roadmap.
• Ensure integration with enterprise systems and regulatory compliance.
• Own AI monitoring across InfraOps and MLOps and lead remediation.
• Mentor junior engineers and lead code reviews; define standards and security practices.

Skills

Coding proficiency

AI/ML infrastructure

Python/Java/C++

CI/CD pipelines

Cloud and on-prem

Monitoring and debugging

Education

Bachelor's Degree in CS/Engineering

Tools

Docker

Kubernetes

Apache Airflow

Kubeflow

Model serving frameworks

Job description

AI Infrastructure Architect

Industry: Infrastructure & Capital Projects Engineering (Construction / Infrastructure / Capital Projects)

and

YOU ARE

As a hands-on AI 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.