At a Glance
- Tasks: Join our team to design and optimise AI training and inference clusters using Kubernetes and Slurm.
- Company: Dynamic tech company focused on AI infrastructure and innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with exciting projects and career advancement opportunities.
- Why this job: Be at the forefront of AI technology and make a real impact in the field.
- Qualifications: Strong Kubernetes and Slurm expertise, plus programming skills in Python and C++.
The predicted salary is between 60000 - 80000 € per year.
Requirements
- Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management
- Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization
- Experience with deploying and managing distributed training systems at scale
- Deep understanding of container orchestration and distributed systems architecture
- High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies)
- Experience managing GPU clusters and optimizing compute resource utilization
- Expert-level Kubernetes administration and YAML configuration management
- Proficiency with Slurm job scheduling, resource management, and cluster configuration
- Python and C++ programming with focus on systems and infrastructure automation
- Hands-on experience with ML frameworks such as PyTorch in distributed training contexts
- Strong understanding of networking, storage, and compute resource management for ML workloads
- Experience developing APIs and managing distributed systems for both batch and real-time workloads
- Solid debugging and monitoring skills with expertise in observability tools for containerized environments
- (Desirable) Experience with Kubernetes operators and custom controllers for ML workloads
- (Desirable) Advanced Slurm administration including multi-cluster federation and advanced scheduling policies
- (Desirable) Familiarity with GPU cluster management and CUDA optimization
- (Desirable) Experience with other ML frameworks like TensorFlow or distributed training libraries
- (Desirable) Background in HPC environments, parallel computing, and high-performance networking
- (Desirable) Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices
- (Desirable) Experience with container registries, image optimization, and multi-stage builds for ML workloads
- Demonstrated experience managing large-scale Kubernetes deployments in production environments
- Proven track record with Slurm cluster administration and HPC workload management
- Previous roles in SRE, DevOps, or Platform Engineering with focus on ML infrastructure
- Experience supporting both long-running training jobs and high-availability inference services
- Ideally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource management
What the job involves
- We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS.
- As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters.
- Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads.
- Manage and optimize Slurm-based HPC environments for distributed training of large language models.
- Develop robust APIs and orchestration systems for both training pipelines and inference services.
- Implement resource scheduling and job management systems across heterogeneous compute environments.
- Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure.
- Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm.
- Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services.
- Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands.
AI Infrastructure Engineer (Member of Technical Staff) in London employer: Deepstreamtech
Join a forward-thinking company that prioritises innovation and collaboration, making it an exceptional employer for AI Infrastructure Engineers. With a strong focus on employee growth, we offer extensive training opportunities and a supportive work culture that encourages creativity and teamwork. Located in a vibrant tech hub, our team enjoys access to cutting-edge resources and a dynamic environment that fosters professional development and meaningful contributions to the field of AI.
StudySmarter Expert Advice🤫
We think this is how you could land AI Infrastructure Engineer (Member of Technical Staff) in London
✨Tip Number 1
Network like a pro! Attend meetups, webinars, or tech conferences related to AI and infrastructure. Chat with folks in the industry, share your experiences, and don’t be shy about asking for advice or referrals.
✨Tip Number 2
Show off your skills! Create a GitHub repository showcasing your projects, especially those involving Kubernetes, Slurm, or any ML frameworks. This gives potential employers a peek into your hands-on experience and problem-solving abilities.
✨Tip Number 3
Tailor your approach! When reaching out to companies, mention specific projects or technologies they use that excite you. This shows you’ve done your homework and are genuinely interested in their work.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it’s a great way to ensure your application gets the attention it deserves.
We think you need these skills to ace AI Infrastructure Engineer (Member of Technical Staff) in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with Kubernetes, Slurm, and any relevant programming skills. We want to see how your background aligns with the role, so don’t be shy about showcasing your expertise!
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 at StudySmarter. Keep it engaging and relevant to the job description.
Showcase Your Projects:If you've worked on any projects involving distributed training systems or Kubernetes management, make sure to mention them. We love seeing real-world applications of your skills, so include links or descriptions of your work!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Deepstreamtech
✨Know Your Kubernetes Inside Out
Make sure you brush up on your Kubernetes skills, especially around custom resource definitions and operators. Be ready to discuss your hands-on experience with cluster management and how you've tackled challenges in previous roles.
✨Show Off Your Slurm Skills
Prepare to talk about your experience with Slurm workload management. Highlight specific examples of job scheduling and resource allocation you've handled, and be ready to explain how you optimised clusters for better performance.
✨Demonstrate Your Programming Prowess
Since Python and C++ are key for this role, come prepared with examples of how you've used these languages for systems and infrastructure automation. Discuss any projects where you developed APIs or worked with ML frameworks like PyTorch.
✨Be Ready for Technical Challenges
Expect some technical questions or scenarios during the interview. Practice explaining complex concepts like distributed training strategies and GPU cluster management clearly and concisely, as this will showcase your deep understanding of the field.
