At a Glance
- Tasks: Join us to build and support AI infrastructure while learning hands-on from experienced engineers.
- Company: Dynamic tech company focused on AI and machine learning innovation.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Collaborative environment with mentorship and excellent career advancement opportunities.
- Why this job: Make a real impact in the AI field while developing your technical skills.
- Qualifications: Bachelor's degree in Computer Science or related field; coding experience required.
The predicted salary is between 60000 - 80000 £ per year.
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.
Responsibilities- 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, or related engineering field.
- Basic (Required) Qualification: Work or coursework 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 as a subject; preferred knowledge of AI infrastructure.
- Proficiency in programming languages such as Python, Java, or C++.
- London
- Berlin
- Madrid
- Paris
Equal Employment Opportunity Statement: All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law. Job candidates will not be obligated to disclose sealed or expunged records of conviction or arrest as part of the hiring process.
AI Infrastructure Junior Architect employer: Accenture
As an AI Infrastructure Junior Architect, you will join a dynamic and innovative team in a vibrant city, where hands-on learning and mentorship are at the forefront of our work culture. We offer a collaborative environment that encourages personal and professional growth, with access to cutting-edge technologies and real-world applications in AI and machine learning. Our commitment to diversity and inclusion ensures that every employee feels valued and empowered to contribute meaningfully to impactful projects.
StudySmarter Expert Advice🤫
We think this is how you could land AI Infrastructure Junior Architect
✨Join Developer Communities
Get involved in online developer communities like GitHub or Stack Overflow. We can showcase our skills by contributing to open-source projects – it’s a great way to network, learn, and possibly catch the eye of a recruiter while doing something we love!
✨Attend Coding Meetups and Hackathons
Check out local coding meetups and hackathons. These events are fantastic for meeting other developers and potential employers, plus they're a great way to get some hands-on experience and showcase our problem-solving skills in real-time.
✨Set Up a Public Portfolio
We should create a public portfolio or GitHub repository showcasing our projects and code. This not only demonstrates our technical skills but also gives employers a peek into our creative process and problem-solving abilities.
✨Utilise University Career Services
If we're fresh out of uni, let's not forget about our university’s career services! They often have tailored resources and connections in the software development field. Plus, internships can lead to entry-level roles – a true win-win!
We think you need these skills to ace AI Infrastructure Junior Architect
Some tips for your application 🫡
Show Off Your Coding Skills:As this is an entry-level role in software engineering development, make sure to include your coding projects. Whether it's a cool school project, a personal website, or even contributions to open-source, it all counts! Link to your GitHub or any platforms you've showcased your code on – we want to see what you've got!
Tailor Your CV to Highlight Relevant Skills:Make your CV work for you by focusing on the programming languages and frameworks you've learned. If you've dabbled in JavaScript, Python, or any specific frameworks, be sure to include those. Plus, showcasing any relevant coursework or certifications can really help us get a clearer picture of your skill set.
Craft a Motivating Cover Letter:Since you're applying for an entry-level position, your cover letter is your chance to shine. Tell us why you’re passionate about software engineering and what excites you about working with Accenture. Highlight any internships or projects that shaped your interest in coding – it’s all about your motivation!
Use Your Network:Don't hesitate to mention any connections you might have to Accenture in your application. If you know someone who works there or have attended any events they hosted, slip that into your cover letter. It shows your genuine interest and can give you that extra edge in your application!
How to prepare for a job interview at Accenture
✨Know Your Code: Prepare for Technical Questions
For a role in software engineering, you can bet your Interviewer might throw some coding problems your way. Brush up on common algorithms and data structures, and practise coding on platforms like LeetCode or HackerRank. That way, you're ready to showcase your problem-solving skills confidently!
✨Portfolio Power: Show Off Your Projects
As an entry-level candidate, your portfolio is your secret weapon. Make sure you have a few solid projects on GitHub that demonstrate your coding skills and understanding of software development processes. Be ready to walk through your code and explain your thought process during the interview.
✨Familiarise Yourself with Agile and Development Tools
Understanding Agile methodologies can really set you apart from other entry-level candidates. Get familiar with tools like JIRA or Trello, and be prepared to discuss how you've used them in your projects or studies. This shows you're not just a coder but also a team player.
✨Demonstrate Your Learning Mindset
Since you're applying for an entry-level position, it's important to show your eagerness to learn. Be ready to discuss how you’ve tackled challenges in your studies or projects, what new skills you’ve picked up recently, and how you plan to continue developing in this fast-paced field.
