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- Tasks: Lead technical engagements and design scalable AI infrastructure solutions.
- Company: Join a leading tech firm at the forefront of AI innovation.
- Benefits: Competitive salary, annual bonus, and opportunities for professional growth.
- Other info: Dynamic work environment with exciting projects and career advancement potential.
- Why this job: Make a real impact in AI while collaborating with top industry professionals.
- Qualifications: Experience in customer-facing technical roles and strong knowledge of AI infrastructure.
The predicted salary is between 90000 - 110000 £ per year.
We're looking for 3 Solutions Architects to lead technical engagements across its growing AI infrastructure practice. This is an integral pre-sales role that sits between technical depth and commercial delivery translating complex customer challenges into scalable, production-ready AI infrastructure solutions.
You will work on large-scale GPU deployments, distributed training and inference architectures, high-performance networking fabrics, and the software stack that makes it all run. The business has the relationships, credentials, and pipeline to give this hire immediate traction.
You’ll partner closely with account teams throughout the full sales cycle. This role requires equal parts technical depth and commercial acumen. You will need to be as comfortable presenting ROI trade-offs and risk summaries to a CTO as you are discussing GPU memory bandwidth constraints or RDMA topology with an infrastructure engineering team.
- Run structured discovery to understand AI workload goals (training vs. inference).
- Qualify opportunities early and advise account teams on architecture direction, risk, and deal strategy.
- Design end-to-end AI infrastructure architectures spanning GPU-accelerated compute, networking (Ethernet/InfiniBand/RoCE), storage, and the full software stack.
- Author and review technical proposal content: architecture diagrams, BOM guidance, SoW inputs, risk/mitigation summaries, and acceptance criteria.
- Help account teams articulate differentiation on performance, TCO, time-to-value, and supportability in competitive situations.
- Ensure clean transitions from pre-sales to implementation with validated designs, clear requirements, and agreed acceptance criteria.
Proven experience in a customer-facing technical role supporting complex infrastructure or platform deals. Strong technical literacy across enterprise and AI infrastructure: GPU servers, PCIe/NVLink-class interconnects, networking fabrics (Ethernet, InfiniBand, RoCE), storage fundamentals, and Linux-based operations.
Ability to translate workload and business goals into practical architectures with clear assumptions, trade-offs, and sizing across performance, cost, power, reliability, and security. Consultative engagement skills: able to lead technical conversations, handle objections, and partner with account teams to progress and close deals.
Genuine curiosity about AI infrastructure: stays current on accelerator platforms, model trends, and the relationship between infrastructure choices and training/inference performance. Hands-on experience with AI/HPC orchestration platforms (Kubernetes, Slurm, Ray) and familiarity with GPU software stacks, CUDA ecosystem, container runtimes, driver management, libraries.
Knowledge of networking for distributed AI workloads. Experience producing pre-sales assets: reference architectures, sizing guides, benchmark reports, TCO/ROI models, and competitive battlecards. Exposure to enterprise buying requirements: security/compliance, on-premises deployment constraints, procurement processes, and hardware lifecycle/lead-time realities. Relevant vendor or industry certifications are a bonus.
Solutions Architect (C#, .NET) employer: asobbi
Contact Detail:
asobbi Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Solutions Architect (C#, .NET)
✨Tip Number 1
Network like a pro! Get out there and connect with industry folks on LinkedIn or at events. We all know that sometimes it’s not just what you know, but who you know that can help you land that Solutions Architect gig.
✨Tip Number 2
Show off your skills in real-time! Consider setting up a portfolio or a GitHub repository showcasing your projects related to AI infrastructure. This way, when you chat with potential employers, you can back up your claims with tangible evidence of your expertise.
✨Tip Number 3
Practice makes perfect! Prepare for interviews by role-playing common scenarios you might face as a Solutions Architect. We suggest getting a mate to throw some tough questions your way, especially around technical depth and commercial delivery.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets the attention it deserves. Plus, we’re always on the lookout for passionate individuals who can translate complex challenges into scalable solutions.
We think you need these skills to ace Solutions Architect (C#, .NET)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the Solutions Architect role. Highlight your experience with AI infrastructure, GPU deployments, and any relevant technical skills. We want to see how your background aligns with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI infrastructure and how your skills can help us tackle complex customer challenges. Keep it engaging and personal – we love a good story!
Showcase Your Technical Skills: In your application, don’t shy away from showcasing your technical depth. Mention specific projects or experiences where you’ve designed end-to-end architectures or worked with GPU servers. We’re keen to see your hands-on experience!
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you don’t miss out on any important updates. Plus, it’s super easy!
How to prepare for a job interview at asobbi
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technical aspects of AI infrastructure, especially around GPU deployments and networking fabrics. Brush up on your knowledge of CUDA, Kubernetes, and the latest trends in AI workloads. Being able to discuss these topics confidently will show that you’re not just a candidate, but a potential expert.
✨Prepare for the Commercial Side
Since this role requires a balance between technical depth and commercial acumen, be ready to discuss ROI trade-offs and risk summaries. Think about how you can articulate the value of your solutions in terms of performance, TCO, and time-to-value. This will demonstrate your understanding of the business side of tech.
✨Practice Structured Discovery
Get comfortable with running structured discovery sessions. Prepare questions that help uncover customer goals and challenges related to AI workloads. This skill is crucial for advising account teams on architecture direction and deal strategy, so practice makes perfect!
✨Show Your Curiosity
Demonstrate your genuine interest in AI infrastructure by staying updated on the latest accelerator platforms and model trends. Bring examples of how you've kept your knowledge current, whether through courses, webinars, or personal projects. This enthusiasm can set you apart from other candidates.