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Описание вакансии
Текст:
TL;DR
AI Reliability Engineer (HPC) (AI/HPC): Building and operating reliable, fault-tolerant infrastructure for large-scale AI model training and inference with an accent on observability, automation, GPU clusters, and distributed systems. Focus on leading incident response, improving capacity and cost efficiency, securing production environments, and accelerating research-to-production workflows.
Location: London, United Kingdom. Employees living within 25 miles of the designated Microsoft office are expected to work from the office at least four days per week, subject to local law.
Salary: £93,500–£161,800 per year.
Company
Microsoft AI develops AI systems and infrastructure that support research, products, and large-scale model deployment.
What you will do
- Ensure uptime, resiliency, and fault tolerance for HPC clusters supporting AI model training and inference.
- Design and maintain monitoring, alerting, and logging for GPU clusters, storage, networking, and related infrastructure.
- Build automation for deployments, incident response, scaling, and failover across CPU and GPU environments.
- Lead on-call rotations, troubleshoot production incidents, conduct blameless postmortems, and drive reliability improvements.
- Maintain secure, compliant operations and protect data across model training and serving environments.
- Partner with ML engineering and platform teams to improve developer experience and research-to-production workflows.
Requirements
- Bachelor’s degree in Computer Science or a related technical discipline and 4+ years of experience in Site Reliability Engineering, DevOps, or Infrastructure Engineering, or equivalent experience.
- Experience with Kubernetes, Docker, container orchestration, and CI/CD pipelines for ML training or inference workloads.
- Experience with public cloud platforms such as Azure, AWS, or GCP and infrastructure as code.
- Experience with monitoring and observability tools such as Grafana, Datadog, or OpenTelemetry.
- Programming or scripting experience in Python, Go, or Bash.
- Experience with distributed systems, networking, storage, HPC, GPU clusters, workload schedulers, ML pipelines, capacity planning, or GPU infrastructure cost optimization.
Culture & Benefits
- Work on AI infrastructure supporting large-scale model training and inference.
- Collaborate with product, ML engineering, platform, and research teams.
- Employment may include benefits and other compensation.
- Microsoft provides equal employment opportunity and reasonable accommodation support.
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