AI Infrastructure Architect Cybersecurity (All Genders)

Accenture DACH; Zürich; Hybrid; CHF 180,000 - 280,000; Full time

Benefits offered by this job

Flexible home office

Technical and soft-skill training

Certifications and learning platforms

Job summary

Accenture DACH is seeking an AI Infrastructure Architect to design, implement, and optimize AI infrastructure across cloud and on‑premise environments. You will build GPU clusters, deploy models, and lead deployment pipelines while driving security and performance improvements.

The role requires hands‑on software engineering, architecture skills, and the ability to mentor junior engineers in InfraOps and MLOps practices. A strong CI/CD ethos and collaboration with enterprise teams are essential.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or a related field.
• Hands-on coding, monitoring, and troubleshooting AI/ML applications and their infra.
• Strong knowledge of AI/ML infrastructure concepts and cloud/on‑prem deployment.
• Experience in cybersecurity and secure design of AI systems.
• Proficiency in Python, Java, or C++, plus experience with data pipelines.

Responsibilities

• Write, review, and debug code and IaC for AI infra and tooling.
• Provision compute resources across cloud and on‑prem environments, incl. GPU clusters.
• Design and maintain CI/CD pipelines for AI systems and deployments.
• Deploy AI models and data pipelines into production with best practices.
• Lead container orchestration with Docker and Kubernetes; guide model serving.
• Optimize stack for performance, power, cost, and scalability.
• Evaluate tools/frameworks and shape the infrastructure roadmap.
• Ensure interoperability, security, and regulatory compliance across systems.
• Own AI monitoring and infra health across InfraOps and MLOps; drive remediation.
• Mentor junior engineers and conduct code reviews; document architecture standards.

Skills

Python

Java

C++

AI/ML infra

Cybersecurity

Mentoring

Code reviews

Root-cause analysis

Education

Bachelor's degree in CS/CE

Tools

Docker

Kubernetes

CI/CD pipelines

Apache Airflow

Kubeflow

Job description

AI Infrastructure Architect - Cybersecurity

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Your Mission

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.

Your Daily 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.

Who You Are

• Bachelor's Degree in Computer Science, Computer Engineering, related Engineering field
• 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.
• Experience in Cybersecurity Industry
• 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.

What We Offer

• Flexible working models, including home office
• Access to over 30,000 technical and soft‑skills training opportunities
• Certifications and learning platforms

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Your unique combination of skills, personality, and aspirations sets you apart. When distinct talents, strengths, and perspectives merge, the result is nothing short of extraordinary: ideas that propel the world forward. This is why we champion diversity and personal development at Accenture. Create a working environment where you truly thrive; with responsibilities that resonate deeply; with a workload that is perfectly aligned with your capabilities; and methods that are tailored to your objectives. How you wish to connect, further your development, and personally evolve rests in your hands. This is your career. We are mere facilitators, helping you shape it exactly as you envision.