At a Glance
- Tasks: Design and build our in-house AI environment while leading a team of engineers.
- Company: Ambitious tech company focused on next-gen AI products.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Lead and mentor a team while establishing best practices in AI infrastructure.
- Why this job: Shape the future of AI with cutting-edge technologies and make a real impact.
- Qualifications: 5+ years in machine learning or AI system design with strong programming skills.
The predicted salary is between 63000 - 77000 £ per year.
AI Solutions Architect - Build Our In-House AI Environment
Location: London (onsite) Type: Full-time | Permanent Salary: Up to £110k total compensation
About Us
We're an ambitious technology company using advanced Machine Learning (ML) and Natural Language Processing (NLP) to power next-generation products.
Until now, our AI projects have relied on third-party engines deployed via Docker - but it's time to take the next step.
We're ready to build our own in-house AI environment, giving us full control over our models, data, and innovation pipeline.
To make that happen, we're looking for an AI Infrastructure Lead - someone with the technical depth and vision to architect, implement, and scale an environment capable of supporting multiple ML and NLP initiatives across the business.
The Role
As our first senior AI hire, you'll take ownership of designing and building the foundation of our AI ecosystem.
You'll shape the infrastructure strategy, establish our MLOps pipelines, and work closely with product and data teams to enable seamless model development and deployment.
Once the environment is established, you'll play a key role in recruiting and mentoring two mid-level AI engineers who will join your team.
Responsibilities
- Architect, build, and maintain an in-house AI environment (on-prem or hybrid cloud).
- Design MLOps workflows for training, deploying, and monitoring models.
- Integrate and manage containerized AI engines (Docker/Kubernetes).
- Establish best practices for model versioning, data pipelines, and reproducibility.
- Collaborate with ML and NLP researchers to optimize infrastructure for experimentation.
- Set up CI/CD pipelines, monitoring tools, and scalable compute infrastructure.
- Lead future recruitment and mentoring of additional AI engineers.
Skills & experience Required
- 5+ years of experience in machine learning, data science, or AI system design.
- Proven track record of deploying ML models or LLM-based applications to production.
- Strong programming fundamentals, including data structures and algorithms.
- Hands-on experience with transformers, embeddings, and vector databases.
- Experience running AI workloads on offline or on-premise platforms (non-cloud environments).
- Solid understanding of data pipelines, APIs, and scalable system architecture.
- Experience leading small teams or mentoring other engineers.
- Familiarity with MLOps tools and best practices.
- Background in integrating AI solutions into enterprise products.
- Awareness of privacy, bias mitigation, and model explainability techniques.
What We Offer
- Opportunity to design and own the company's AI infrastructure from the ground up.
- Work with cutting-edge AI technologies in a hands-on, high-impact role.
- Leadership path - build your own AI engineering team.
- Competitive salary (up to £110k), flexible working, and professional growth opportunities.
Ready to build the foundation of our AI future?
AI Engineer employer: Digital Waffle
As a leading ecommerce retailer, we pride ourselves on fostering a dynamic and inclusive work culture that empowers our employees to thrive. With a focus on professional development, we offer ample opportunities for growth and mentorship, particularly for those in project management roles. Located in Hereford, our hybrid working model allows for flexibility while ensuring collaboration with talented teams dedicated to delivering innovative technology solutions.
StudySmarter Expert Advice🤫
We think this is how you could land AI Engineer
✨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 Digital Waffle 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 Digital Waffle.
✨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 Digital Waffle.
✨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 Digital Waffle 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 Engineer
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 Digital Waffle.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Digital Waffle 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 Digital Waffle
✨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 Digital Waffle 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.
