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FlexAI

On-demand GPU clusters for AI workloads

Staff DevOps Engineer/Site Reliability Engineer

Site Reliability EngineeringUpdated on Nov 15
Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

  • At least 8 years of experience in DevOps, Site Reliability Engineering, or Infrastructure Engineering.
  • Proven experience operating large-scale, distributed systems in production.
  • Deep expertise in Kubernetes and container orchestration.
  • Deep expertise in Pulumi or similar infrastructure-as-code tools.
  • Deep expertise in cloud or hybrid environments, including AWS, GCP, Azure, or on-premises environments.
  • Deep expertise in observability stacks, including Prometheus, Grafana, and OpenTelemetry.
  • Strong experience with continuous integration and continuous delivery, automation, and release engineering.
  • Proficiency in Python, Go, or Bash.
  • Strong systems thinking and debugging skills in high-scale environments.
  • Experience defining and operating with service-level objectives and service-level agreements.
  • Experience in startup environments.
  • Comfort using AI coding tools and agents.

Responsibilities

  • Design and evolve the infrastructure backbone for the AI and PaaS platform.
  • Build highly available, fault-tolerant, and scalable systems.
  • Define and drive site reliability engineering practices, including service-level indicators, service-level objectives, and error budgets.
  • Lead infrastructure as code using Pulumi.
  • Own and scale Kubernetes clusters and containerized workloads.
  • Standardize and automate infrastructure for global deployments.
  • Design and scale continuous integration and continuous delivery pipelines for fast, reliable releases.
  • Build self-healing systems and automated remediation workflows.
  • Drive GitOps and platform engineering practices.
  • Implement end-to-end observability using VictoriaMetrics and Grafana for metrics, logs, and traces.
  • Identify and resolve performance bottlenecks involving latency, throughput, and cost.
  • Lead incident response, root cause analysis, and postmortems.
  • Partner with backend, AI, runtime, and security teams.
  • Guide infrastructure decisions and scaling strategy.
  • Mentor engineers and improve reliability and engineering standards.
  • Embed security into infrastructure and deployment workflows.
  • Design for resilience through disaster recovery, chaos testing, and capacity planning.

Desired Qualifications

  • Experience with AI/ML infrastructure or GPU workloads.
  • Familiarity with distributed or high-performance compute systems.
  • Exposure to platform engineering or internal developer platforms.
  • Experience scaling systems from beta to production.

About the Company

FlexAI

On-demand GPU clusters for AI workloads

FlexAI provides a Workload as a Service (WaaS) platform that gives AI developers on-demand access to scalable GPU compute for the full AI lifecycle, including training, fine-tuning, and inference. The platform lets users spin up GPU clusters quickly and run workloads in parallel, with automated scaling to optimize resource use and costs. It also supports performing multiple fine-tuning runs across different datasets and models at the same time, enabling rapid experimentation while the platform handles infrastructure management. This differentiates FlexAI from competitors by offering serverless, pay-per-use AI-optimized compute that abstracts away hardware setup and maintenance, focusing users on building and deploying models. The company aims to accelerate AI initiatives for developers, data scientists, and organizations by delivering cost-efficient, scalable compute without the overhead of managing their own GPUs.

Company size
51-200
Company stage
Seed
Total funding
$30M
Headquarters
Paris, France
Founded
2023

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