Data Science Practitioner

Accenture; Abu Dhabi; On-site; AED 150,000 - 210,000; Full time

Job summary

Accenture in the United Arab Emirates is seeking a hands-on Infrastructure Architect at an early-career level. You will learn by doing, building AI and ML infrastructure that powers real-world applications, under mentorship from senior engineers.

You will write code, configure cloud and on‑prem resources, deploy AI models, monitor systems, and contribute to InfraOps and MLOps practices. The role emphasizes practical skills across Docker, Kubernetes, CI/CD, and scalable, secure infrastructure.

Qualifications

• 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, preferred knowledge of AI infrastructure.
• Proficiency in programming languages such as ML, Python, Java, or C++.

Responsibilities

• Write, test, and debug code and scripts for AI infrastructure tasks under guidance of senior engineers.
• Develop and maintain infrastructure and software deployment scripts to support reliable 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 ML 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.

Skills

AI/ML concepts

Python/Java/C++

Problem solving

Education

Bachelor's Degree in Computer Science/Engineering

Tools

Docker

Kubernetes

CI/CD tooling

Job description

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.

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++.

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.

Requirements