AI Infrastructure Junior Architect

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JuniorOn-site·WarszawaFull-time

#473702·Dodano wczoraj·5

Źródło: Accenture

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Tech Stack / Keywords

AI/MLMLOpsDocker Kubernetes CI/CDGPU clustersInfraOpsPython Java C++

Podsumowanie oferty

As a hands-on infrastructure architect, you will contribute to the AI and machine learning infrastructure that powers real-world applications. You will help configure compute resources, deploy AI systems and models, support data pipelines, and monitor infrastructure using technologies including Docker, Kubernetes, and CI/CD pipelines.

Wymagania

Bachelor's degree in Computer Science, Computer Engineering, or a related engineering field.
Work or coursework experience with AI/ML, computer engineering, or computer science.
Experience coding, building, monitoring, and troubleshooting applications of AI/ML models, and selecting and designing infrastructure for deploying and running them on-premises or on a public cloud.
Strong understanding of AI and machine learning.
Strong understanding of computing infrastructure; knowledge of AI infrastructure is preferred.
Proficiency in programming languages such as Python, Java, or C++.

Obowiązki

Write, test, and debug code and scripts for AI infrastructure tasks, including automation and tooling, under the guidance of senior engineers.
Develop and maintain infrastructure and software deployment scripts to support reliable, repeatable releases of AI systems and models.
Configure and provision compute resources across cloud and on-premises environments, including GPU clusters and distributed training setups.
Deploy AI systems and machine learning models into production infrastructure, following established processes and best practices.
Deploy data pipelines that feed AI and ML workflows, ensuring data is available, clean, and reliable.
Assist with container orchestration and model serving, learning tools such as Docker, Kubernetes, and model deployment frameworks.
Support and maintain CI/CD pipelines for automating the build, test, and deployment of AI infrastructure and applications.
Monitor AI systems and infrastructure health across InfraOps and MLOps disciplines, tracking performance, reliability, and resource utilization.
Perform AI monitoring to track model performance, detect drift or degradation, and surface issues for review.
Troubleshoot and help resolve issues across the computational stack—hardware, networking, software, and models—with mentorship and support.
Document configurations, processes, and procedures to maintain clear, repeatable, and shareable knowledge across the team.
Collaborate with senior architects and engineers, participating in code reviews, team discussions, and knowledge-sharing sessions to grow technical skills.
Apply security, cost-efficiency, and scalability best practices as you learn them, contributing to well-managed and responsible infrastructure.

Accenture

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