NexGen Cloud - Product Manager

• Rather than a checklist, here’s what success in this role actually looks like: • Own and drive the product vision and roadmap for the new customer platform, AI Studio and ML products, prioritising ruthlessly across customer needs, commercial goals and engineering capacity. • Collaborate closely with Engineering, Infrastructure, Sales, Customer Success, Marketing and Security, so products are buildable, supportable, compliant and ready to sell at launch. • Run regular discovery with customers and prospects to understand how they build, train, fine-tune and deploy models, and turn what you learn into clear problem statements, user stories and acceptance criteria. • Define what a minimum useful release looks like for each product, and sequence delivery so customers get value early rather than at the end of a long build. • Lead your team from idea to release: run the planning and delivery cadence, keep the backlog actionable, and remove blockers along the way. • Line-manage and develop three Product Engineers, and help hire and build a team that owns problems end to end. • Define and track adoption, activation, usage and retention metrics, and use them, alongside the competitive landscape, to decide what to do next. • We care more about how you think and work than a perfect CV. You’ll likely bring a combination of the following: • Proven experience as a Product Manager owning technical products end to end, ideally in cloud, developer platforms or infrastructure. • A track record of taking a new product from early concept through to launch and adoption. • Experience line-managing or directly leading a small team, including performance and development conversations. • Enough technical depth to work credibly with engineers on APIs, platform architecture and infrastructure trade-offs. • A working understanding of how AI and ML teams build, train and deploy models, and the tooling they rely on. • Strong discovery and prioritisation skills, with evidence of turning customer research into roadmap decisions. • Product experience with GPU cloud, AI/ML platforms, MLOps tooling, or model serving and fine-tuning products. • A hands-on technical background, such as software or ML engineering, and conceptual familiarity with Kubernetes, containers and the NVIDIA GPU ecosystem. • Experience with usage-based pricing models and working across distributed teams in multiple time zones.