2 месяца назад
Platform Engineering Lead (AI)
140 000 - 195 000GBP
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Platform Engineering Lead (AI): Leading a platform engineering team building and operating Kubernetes-native GPU infrastructure, control-plane services, APIs, storage, networking, and customer-facing platform layers with an accent on technical direction, reliability, security, and scalable infrastructure design. Focus on solving complex systems problems, managing multi-tenant GPU capacity, coordinating cross-team delivery, and closing structural gaps after incidents while remaining hands-on in production engineering.
Location: London, UK; hybrid work arrangement.
Salary: £140,000–£195,000 per year, plus equity and discretionary bonus.
Company
is a vertically integrated AI infrastructure company building large-scale GPU compute infrastructure, Kubernetes-native platforms, virtual machines, storage, and networking for AI workloads.
What you will do
- Lead a platform engineering capability team through technical direction, design reviews, code reviews, delivery oversight, and people management.
- Design and build platform layers, control-plane services, APIs, and infrastructure features as product requirements evolve.
- Represent platform engineering in roadmap planning by scoping technical work, sequencing delivery, and communicating trade-offs.
- Partner with bring-up teams to turn operational pain points into scalable platform features.
- Coordinate shared architecture and cross-team delivery with other platform engineering leads and security engineering.
- Own reliability, observability, security, interface standards, versioning practices, incident post-mortems, hiring, onboarding, and team development.
Requirements
- 5+ years of cloud platform or infrastructure engineering experience, including at least 2 years leading an engineering team.
- Strong backend or systems programming experience in a production environment; working languages include Python, Go, and Rust.
- Deep production Kubernetes experience covering cluster operations, CNI networking, scheduling, and workload management.
- Experience with GPU infrastructure, including provisioning, resource allocation, and exposing capacity through a platform or API layer.
- Strong networking fundamentals covering L2/L3, VLANs, overlay networks, and multi-tenant isolation.
- Experience designing and operating production-grade control-plane services and APIs, plus proven people management and technical communication skills.
Nice to have
- AI-assisted development and agent-assisted engineering workflows.
- Confidential computing technologies such as TEEs, AMD SEV, Intel TDX, or Confidential Containers.
- SaaS or PaaS layers built on IaaS platforms, serverless infrastructure, or inference serving.
- RDMA, InfiniBand, or RoCE networking in GPU or HPC clusters.
- Experience working across time zones with distributed counterparts.
Culture & Benefits
- Equity in and compensation based on the work performed rather than previous salary.
- Retirement or pension contributions.
- Health, wellbeing, and insurance benefits.
- Generous annual vacation allowance.
- Supportive, trusted, growth-oriented work environment with attention to work-life balance.
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