At a Glance
- Tasks: Design and optimise AI infrastructure for large-scale machine learning systems.
- Company: Join a leading tech firm at the forefront of AI innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Diverse and inclusive workplace with a focus on collaboration and creativity.
- Why this job: Be a key player in shaping the future of AI technology.
- Qualifications: Experience in AI/ML infrastructure and strong coding skills required.
The predicted salary is between 60000 - 80000 £ per year.
You are responsible for designing optimized compute infrastructure for large-scale AI and machine learning systems, including large-scale distributed training environments. You translate business goals, SLAs, and client standards into infrastructure architectures that perform at scale while being engineered for cost-efficiency. You weigh multiple viable solutions across compute, networking, storage, orchestration, and model serving, making rational architectural decisions tailored to each client’s situation, constraints, and standards.
Your responsibilities include:
- Architecting and optimizing the full computational stack for performance, power, cost, and scalability.
- Designing and tuning large-scale GPU clusters and distributed training systems.
- Ensuring infrastructure meets security, compliance, and regulatory requirements.
- Bringing authoritative knowledge of at least one hyperscaler cloud (such as AWS, Azure, or Google Cloud) and applying it to deliver best-in-class solutions.
- Setting technical direction and standards, leading and mentoring engineers and architects.
- Partnering with clients and stakeholders to shape the infrastructure roadmap.
- Being accountable for delivering AI/ML infrastructure that meets business SLAs, controls cost, and scales to enterprise workloads.
The work involves:
- Owning the end-to-end architecture and design of optimized compute infrastructure for large-scale AI/ML systems.
- Developing and evaluating architecture alternatives, weighing trade-offs across various domains.
- Leading architecture assessments and reviews of existing and proposed environments.
- Driving architectural decision-making and documenting rationale, trade-offs, and assumptions.
- Defining and maintaining the AI infrastructure roadmap.
- Designing deployment, automation, and CI/CD strategies for reliable releases of AI systems.
- Establishing AI monitoring and observability strategy across InfraOps and MLOps.
- Integrating AI/ML systems into enterprise environments.
- Leading capacity planning and cost modeling.
- Collaborating with clients, stakeholders, and engineering teams.
- Setting technical direction, standards, and best practices.
Education: Bachelor's Degree in Computer Science, Computer Engineering, or related Engineering field.
Basic qualifications include:
- Solid background in coding, building, monitoring, and troubleshooting applications of AI/ML models.
- Strong understanding of AI and machine learning.
- Good proficiency in programming languages such as Python, Java, or C++.
- Experience with data pipeline and workflow management tools.
- Strong problem-solving skills and ability to work in a fast-paced environment.
- Excellent communication and collaboration skills.
- Significant experience in AI/ML infrastructure engineering on a hyperscaler platform.
- Proven experience in leading and managing AI projects and teams.
- Strong project management skills.
- Demonstrated experience in evaluating and selecting AI technologies and frameworks.
- Ability to work with cross-functional teams.
We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to any protected basis as per applicable law. Our rich diversity makes us more innovative, competitive, and creative, helping us better serve our clients and communities.
AI Infrastructure Lead Architect in London employer: Accenture UK
As an AI Infrastructure Lead Architect, you will thrive in a dynamic and inclusive work environment that champions innovation and collaboration. Our company prioritises employee growth through continuous learning opportunities and mentorship, ensuring you stay at the forefront of AI technology. Located in a vibrant tech hub, we offer competitive benefits and a culture that values diversity, making it an exceptional place for professionals seeking meaningful and rewarding careers.
StudySmarter Expert Advice🤫
We think this is how you could land AI Infrastructure Lead Architect in London
✨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 Accenture UK 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 Accenture UK.
✨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 Accenture UK.
✨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 Accenture UK 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 Lead Architect in London
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 Accenture UK.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Accenture UK 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 Accenture UK
✨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 Accenture UK 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.
