At a Glance
- Tasks: Design and optimise AI infrastructure for real-world applications while leading your own projects.
- Company: Join a forward-thinking tech company focused on AI and machine learning.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Work in a dynamic environment with excellent career advancement opportunities.
- Why this job: Make a real impact in the AI field and enhance your skills with cutting-edge technology.
- Qualifications: Experience in AI/ML infrastructure, coding, and strong problem-solving abilities required.
The predicted salary is between 60000 - 80000 £ per year.
As a hands-on Infrastructure Architect, you are an experienced engineer with several years in infrastructure engineering who now takes on more complex, higher-impact work designing and optimizing the AI and machine learning infrastructure that powers real-world applications. Working alongside senior architects and engineers—and increasingly leading your own workstreams—you apply proven skills in coding, testing, configuring, deploying, monitoring, and troubleshooting AI systems and the infrastructure they run on.
Day to day, you architect and optimize infrastructure components, write and review code and deployment scripts, design and tune cloud and on-premises compute resources such as GPU clusters and distributed training environments, deploy AI systems and models into production, and build and optimize data pipelines that feed AI and ML workflows. You optimize the computational stack for performance, cost, power, and scalability, monitor AI systems and infrastructure health across both InfraOps and MLOps disciplines, perform AI monitoring to track model and system performance, and independently troubleshoot and resolve complex issues across the stack. You also mentor junior engineers, contribute to architectural decisions, and help establish best practices.
This is a hands-on, ownership-driven role where you apply and deepen your 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, review, and debug code, scripts, and infrastructure-as-code for AI infrastructure, automation, and tooling, setting standards for quality across the team.
- Architect, configure, and provision compute resources across cloud and on-premises environments, including GPU clusters and distributed training setups, optimizing for performance and utilization.
- Design and maintain deployment automation and CI/CD pipelines to support reliable, repeatable releases of AI systems, models, and applications.
- Deploy AI systems, models, and data pipelines into production, defining and improving the processes and best practices others follow.
- Lead container orchestration and model serving using tools such as Docker, Kubernetes, and model deployment frameworks.
- Architect and optimize the computational stack for performance, power, cost, and scalability, balancing trade-offs against business goals.
- Evaluate and select tools, frameworks, and platforms, making recommendations that shape the infrastructure roadmap.
- Integrate AI models and systems into existing enterprise systems, ensuring interoperability, security, and regulatory compliance.
- Own AI monitoring and infrastructure health across InfraOps and MLOps, tracking performance, reliability, and utilization, and driving remediation.
- Independently troubleshoot and resolve complex issues across the computational stack—hardware, networking, software, and models—and lead root-cause analysis.
- Mentor junior engineers and lead code reviews, providing technical direction and supporting their growth.
- Define and document architecture standards, processes, and procedures, and apply security, cost-efficiency, and scalability best practices across the infrastructure.
Qualification
Education
Bachelor's Degree in Computer Science, Computer Engineering, or related Engineering field.
Basic (Required) Qualification
- Practical 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++.
- Experience with data pipeline and workflow management tools (e.g., Apache Airflow, Kubeflow).
- Strong problem-solving skills and ability to work in a fast-paced environment.
- Excellent communication and collaboration skills.
- Proven experience in AI/ML infrastructure engineering or related roles on a hyperscaler platform for deploying large-scale solutions.
Locations
London, Berlin, Madrid, Paris
AI Infrastructure Architect employer: WeAreTechWomen
At Accenture, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. As an AI Infrastructure Architect in vibrant cities like London, you will enjoy competitive benefits, opportunities for professional growth, and the chance to work with cutting-edge technologies while mentoring the next generation of engineers. Join us to make meaningful contributions to AI-driven business outcomes in a supportive environment that values diversity and inclusion.
StudySmarter Expert Advice🤫
We think this is how you could land AI Infrastructure Architect
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at WeAreTechWomen or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to WeAreTechWomen.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like WeAreTechWomen.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like WeAreTechWomen that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace AI Infrastructure Architect
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at WeAreTechWomen.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at WeAreTechWomen and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at WeAreTechWomen
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If WeAreTechWomen uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
