Data Science Practitioner

Accenture Middle East; Abu Dhabi; On-site; AED 90,000 - 150,000; Full time

Job summary

Accenture Middle East is seeking a hands-on Infrastructure Architect in Abu Dhabi for an early-career role focused on AI and ML infrastructure. You will learn to code, test, configure, deploy, monitor, and troubleshoot AI systems and the infrastructure they run on, under mentorship from senior staff.

You will configure cloud and on‑prem compute resources, such as GPU clusters and distributed training environments, deploy AI models into production, and support data pipelines feeding AI workflows.

Qualifications

• Bachelor's Degree in Computer Science, Computer Engineering, or related field.
• Proven experience with AI/ML or computer engineering or computer science.
• Strong understanding of computing infrastructure and AI infrastructure concepts.
• Proficiency in programming languages such as Python, Java, or C++.

Responsibilities

• Write, test, and debug code and scripts for AI infrastructure tasks, including automation and tooling.
• Develop and maintain infrastructure and software deployment scripts for reliable AI releases.
• Configure and provision compute resources across cloud and on‑prem environments, including GPU clusters.
• Deploy AI systems and ML models into production and manage data pipelines for AI/ML workflows.
• Assist with container orchestration and model serving, learning tools such as Docker and Kubernetes.
• Support and maintain CI/CD pipelines for automating build, test, and deployment of AI infrastructure and apps.
• Monitor AI systems and infrastructure health, track performance and resource utilization.
• Troubleshoot issues across hardware, networking, software, and models with mentorship.
• Document configurations, processes, and procedures to maintain repeatable knowledge across the team.
• Collaborate with senior architects and engineers in code reviews and knowledge sharing.
• Apply security, cost-efficiency, and scalability best practices as you learn them.

Skills

Python

AI/ML concepts

Analytical thinking

Learning agility

Education

Bachelor's Degree in Computer Science/Engineering

Tools

Docker

Kubernetes

CI/CD

Job description

YOU ARE

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.

YOU ARE

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.

THE WORK

• 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.

EDUCATION

• Bachelor's Degree in Computer Science, Computer Engineering, related Engineering field

Basic (required) Qualification

• proven experience with AI/ML or Computer engineering or Computer science.


• 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 ML, Python, Java, or C++.