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Mistral AI

Open-weight LLMs and enterprise AI deployment

Site Reliability Engineer

Site Reliability EngineeringConfirmed live in the last 24 hours
Full-timeNew York, NY, USANo salary listedIn-person

About the Job

Requirements

  • A Master’s degree in Computer Science, Engineering, or a related field is required.
  • At least 7 years of experience in a DevOps or site reliability engineering role is required.
  • Strong experience with cloud computing and highly available distributed systems is required.
  • Exposure to site reliability issues in critical environments, including root cause analysis, in-production troubleshooting, and on-call rotations, is required.
  • Experience working with reliability key performance indicators, observability, alerting, and service-level agreements is required.
  • Hands-on experience with continuous integration and continuous delivery, containerization, and orchestration tools such as Docker and Kubernetes is required.
  • Knowledge of monitoring, logging, alerting, and observability tools such as Prometheus, Grafana, ELK Stack, and Datadog is required.
  • Familiarity with infrastructure-as-code tools such as Terraform or CloudFormation is required.
  • Proficiency in scripting languages such as Python, Go, and Bash, and knowledge of software development best practices, is required.
  • A strong understanding of networking, security, and system administration concepts is required.

Responsibilities

  • Design, build, and maintain scalable, highly available, fault-tolerant infrastructure to support web services and machine learning workloads.
  • Maintain the availability of the platform, inference environments, and model-training environments, and enable seamless replication of work environments across multiple high-performance computing clusters.
  • Operate systems and troubleshoot production issues, including interrupts, on-call responses, user administration, data extraction, and infrastructure scaling.
  • Implement and improve monitoring, alerting, and incident-response systems to optimize performance and minimize downtime.
  • Implement and maintain continuous integration and continuous delivery, containerization, orchestration, monitoring, logging, and alerting workflows and tools for client-facing application programming interfaces and large training runs.
  • Participate occasionally in on-call rotations, respond to incidents, and perform root cause analysis to prevent future incidents.
  • Drive continuous improvement in infrastructure automation, deployment, and orchestration using tools such as Kubernetes, Flux, and Terraform.
  • Collaborate with artificial intelligence and machine learning researchers to develop and implement solutions that enable safe and reproducible model-training experiments.
  • Build a cloud-agnostic platform that provides an abstraction layer between science and infrastructure.
  • Design and develop workflows and tools to improve system reliability, availability, and performance, including automation scripts, refactoring, application programming interface features, web applications, and dashboards.
  • Collaborate with the security team to ensure infrastructure adheres to security best practices and compliance requirements.
  • Document processes and procedures to ensure consistency and knowledge sharing across the team.
  • Contribute to open-source projects, research publications, blog articles, and conferences.

Desired Qualifications

  • Experience in an artificial intelligence or machine learning environment is desirable.
  • Experience with high-performance computing systems and workload managers such as Slurm is desirable.
  • Experience working with modern artificial intelligence-oriented solutions such as Fluidstack, CoreWeave, or Vast is desirable.

About the Company

Mistral AI

Open-weight LLMs and enterprise AI deployment

Mistral AI builds open-weight large language models and the tools to run them, along with inference infrastructure and enterprise products that let organizations deploy AI with control over data residency, customization, and the underlying stack. Its platform provides open-model architectures and a deployment stack for inference, governance, and integration that can run on‑premises or in the cloud. This openness and modular stack differentiate it from single-vendor hyperscalers by prioritizing data locality and flexibility. The company aims to enable reliable, customizable AI deployment at scale for businesses and public-sector customers across many countries.

Company size
1,001-5,000
Company stage
Series D
Total funding
$7.2B
Headquarters
Paris, France
Founded
2023

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