Site Reliability Engineer, Mistral AI

• 10+ years of experience in a DevOps/SRE role
• Experience with building and leading high-performing teams
• Experience with cloud computing and highly available distributed systems
• Exposure to site reliability issues in critical environments (issue root cause analysis, in-production troubleshooting, on-call rotations...)
• Experience working against reliability KPIs (observability, alerting, SLAs)
• Hands-on experience with CI/CD, containerization and orchestration tools (Docker, Kubernetes...), monitoring, logging, alerting and observability tools (Prometheus, Grafana, ELK Stack, Datadog...), infrastructure-as-code tools (Terraform, CloudFormation...)
• Proficiency in scripting languages (Python, Go, Bash...) and knowledge of software development best practices
• Understanding of networking, security, and system administration concepts
• Excellent problem-solving and communication skills
• Self-motivated and able to work well in a fast-paced startup environment

Desirable

• We are seeking a Lead Site Reliability Engineer (SRE) to drive our infrastructure team in their mission to build a reliable, fault tolerant and scalable infrastructure
• You will be responsible for ensuring the reliability of sites in critical distributed environments and improving how our customers interact with our core products
• Reporting line: Head of Engineering
• As a Lead Site Reliability Engineer, you balance team supervision, project management, day-to-day operations on production systems with long-term software engineering improvements to reduce operational toil and foster the reliability, availability, and performance of these systems
• Team Leadership (33%)
• Empower Your Team: act as a facilitator and orchestrator, removing obstacles and fostering an environment where your team can focus on creating impactful solutions
• Thought Leadership: support your team by delivering great experiences while hiring, onboarding, and all-around elevating your people
• Project planning (roadmap, deadlines, task allocation...)
• Collaborating with stakeholders (engineering, science, product management…)
• Operations (33%)
• Design, build, and maintain scalable, highly available and fault-tolerant infrastructures to support our web services and ML workloads
• Make sure our platform, inference and model training environments are always highly available and enable seamless replication of work environments across several HPC clusters
• Operate systems and troubleshoot issues in production environments (interrupts, on-call responses, users admin, data extraction, infrastructure scaling, etc.)
• Implement and improve monitoring, alerting, and incident response systems to ensure optimal system performance and minimize downtime
• Implement and maintain workflows and tools (CI/CD, containerization, orchestration, monitoring, logging and alerting systems) for both our client-facing APIs and large training runs
• Participate occasionally in on-call rotations to respond to incidents and perform root cause analysis to prevent future occurrences
• Development (33%):
• Drive continuous improvement in infrastructure automation, deployment, and orchestration using tools like Kubernetes, Flux, Terraform
• Collaborate with AI/ML researchers to develop and implement solutions that enable safe and reproducible model-training experiments
• Build a cloud-agnostic platform offering an abstraction layer between science and infrastructure
• Design and develop new workflows and tooling to improve to the reliability, availability and performance of our systems (automation scripts, refactoring, new API-based features, web apps, dashboards, etc.)
• Collaborate with the security team to ensure infrastructure adheres to best security 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

• Intro Call (30min)
• Tech Culture Interview (30min)
• Technical Rounds - System Design Interview (45min) - Deep Dive Interviews (2 x 45min)
• Culture-fit discussion (30min)