Digital Core_IIS_15k_ AI Infra Arch_CL11_Czech

As a hands on Infrastructure architect, you are an early-career engineer who learns and grows while contributing hands-on to the AI and machine learning infrastructure that powers real-world applications.  Under the guidance of senior architects and engineers, you'll develop practical skills in coding, testing, configuring, deploying, monitoring, and troubleshooting AI systems and the infrastructure they run on. Day to day, you'll write and test code and deployment scripts, help configure cloud and on-premises compute resources such as GPU clusters and distributed training environments, deploy AI systems and models into production, and support data pipelines that feed AI and ML workflows. You'll learn to monitor AI systems and infrastructure health across both InfraOps and MLOps disciplines, perform AI monitoring to track model and system performance, and troubleshoot issues across the computational stack with mentorship and support. This is a hands-on, learning-focused role where you build 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. - Minimum of 1 year of work or coursework experience with AI/ML or Computer engineering or Computer science. - Minimum of 1 year of 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++. 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 shareholders, partners and communities. Visit us at www.accenture.com Equal Employment Opportunity Statement We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, sexual orientation, gender identity or expression, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.

Summary

Hands-on infrastructure architect building and maintaining AI/ML infrastructure, including cloud and on-premises compute, deployment, monitoring, and troubleshooting. Requires at least one year of AI/ML or computer engineering/science experience, one year coding and operating AI/ML models, and proficiency in Python, Java, or C++.

Responsibilities

Write and test code and deployment scripts; configure cloud and on-premises compute such as GPU clusters and distributed training environments; deploy AI systems and models; support data pipelines; monitor AI systems and infrastructure health; perform AI monitoring; troubleshoot across the computational stack.

Qualifications

Minimum 1 year of work or coursework experience in AI/ML or computer engineering/science; minimum 1 year coding, building, monitoring and troubleshooting AI/ML model applications; strong understanding of AI/ML and computing infrastructure; proficiency in Python, Java, or C++.

Experience requirements

Minimum 1 year of work or coursework experience with AI/ML, computer engineering, or computer science; minimum 1 year of coding, building, monitoring, and troubleshooting applications of AI/ML models.