Solutions Engineer Sales · London

Nexgencloud
London
2 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer - Manchester - Hybrid - £75k - £80k

Data Engineer / Analytics Engineer

Snowflake Data Engineer

Data Engineer

NexGen Cloud is a rapidly growing IaaS company focused on providing innovative cloud solutions and infrastructure services. Our GPU cloud infrastructure solutions accelerate development in industries such as Artificial Intelligence & Machine Learning, VFX & Rendering, Data Science & IoT, and Computer Aided Engineering & MDO.

We are dedicated to helping our clients navigate the complexities of the digital world and achieve success through cutting-edge, scalable, secure and affordable solutions.

At the company's heart stands a group of very talented, experienced, and motivated individuals who want to make a positive change and a lasting impact on the tech world.

Position Summary:

As a Solutions Engineer specializing in Generative AI, you will work within the Customer Experience team while closely aligning with Sales to engage customers, qualify opportunities, and drive successful sales conversions. Your role involves understanding client needs, demonstrating NexGen’s AI and GPU solutions, and providing technical guidance throughout the sales process. The primary focus of the role is solution design, customer engagement, and technical enablement in the AI/ML space, with occasional hands-on technical support as needed.

Key Responsibilities:

  • Customer Engagement – Collaborate with Sales to identify technical requirements related to Gen AI, LLMs, and HPC.
  • Act as a trusted advisor by aligning solutions with strategic business goals and addressing customer pain points.
  • Solution Presentations & PoCs – Develop and deliver compelling demos, presentations, and Proof-of-Concepts (PoCs) tailored to customer needs
  • Technical Expertise – Maintain knowledge of Gen AI frameworks (e.g., PyTorch, TensorFlow), and HPC orchestration tools (e.g., Kubernetes) and GPU virtualization to provide informed guidance.
  • Support & Communication – Collaborate with the Customer Experience team to respond to technical inquiries, ensure smooth PoC handovers and expedite the resolution of VIP customer issues.
  • Proposal & Competitive Analysis – Contribute to RFP responses and technical documentation with a focus on NexGen’s differentiators in Gen AI and HPC, while monitoring market trends to outpace competition.
  • Internal Collaboration – Collaborate with Engineering, Product, and Marketing to refine solutions and messaging based on evolving customer and market needs.
  • Training & Events – Conduct internal training sessions on (Gen) AI and HPC, and represent NexGen at industry events.

Key Requirements:

  • Generative AI & LLMs (Model fine-tuning, inference optimization)
  • GPU Virtualization & HPC (GPU Passthrough, HPC networking with RDMA/SR-IOV, cluster orchestration with Kubernetes/SLURM)
  • NVIDIA Software Stack (CUDA, NCCL, NGC)
  • Optional Cloud & Hybrid Environments (OpenStack as a plus, not a core requirement)
  • Experience in pre-sales, solutions engineering or technical sales with a strong AI/ML focus.
  • Expertise in (Gen) AI, LLM architectures, and GPU-accelerated computing.
  • Excellent communication, problem-solving, and customer-facing skills.
  • Ability to work in a fast-paced, dynamic environment

What We Offer:

  • Competitive salary
  • Opportunity to work with a diverse team of talented professionals who are passionate about technology and innovation.
  • A collaborative and supportive work environment that encourages professional growth and development.
  • Exposure to cutting-edge technologies and the opportunity to make a significant impact on the future of cloud computing.

We encourage applications from candidates of all backgrounds and experiences. Our commitment to diversity and inclusion drives our success as a company and reflects our dedication to fostering a diverse and innovative workforce.

Join our team and become a part of the NexGen Cloud Team, where innovation, collaboration, and growth are at the heart of everything we do. If you are a passionate, talented, and motivated individual looking to make a difference, apply now!

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.