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FlexAI

On-demand GPU clusters for AI workloads

Senior DevOps Engineer/Site Reliability Engineer

DevOps EngineeringUpdated on Nov 13
Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

  • At least 4 years of experience in DevOps, site reliability engineering, or infrastructure engineering.
  • Experience operating production systems at scale.
  • Hands-on experience with Kubernetes and containers.
  • Hands-on experience with infrastructure as code tools such as Pulumi or Terraform.
  • Hands-on experience with cloud or hybrid environments, including AWS, GCP, Azure, or on-premises environments.
  • Hands-on experience with observability tools such as Prometheus, Grafana, and OpenTelemetry.
  • Experience with continuous integration and continuous delivery systems and automation.
  • Proficiency in Python, Go, or Bash.
  • Familiarity with service-level objectives and reliability practices.
  • Experience working in startup or fast-paced environments.
  • Comfort leveraging AI coding tools and agents.

Responsibilities

  • Build and maintain infrastructure for the AI and PaaS platform.
  • Deploy and operate Kubernetes clusters and containerized services.
  • Implement infrastructure as code using Pulumi or similar tools.
  • Help define and implement service-level indicators, service-level objectives, and error budgets.
  • Improve system reliability, availability, and performance.
  • Participate in on-call rotations, incident response, and postmortems.
  • Build and improve continuous integration and continuous delivery pipelines for reliable and fast releases.
  • Automate operational workflows and reduce manual toil.
  • Contribute to GitOps and platform engineering practices.
  • Implement and maintain observability using VictoriaMetrics and Grafana for metrics, logs, and traces.
  • Monitor systems and troubleshoot performance issues involving latency, throughput, and cost.
  • Work with developers, platform teams, and AI teams to support production systems.
  • Debug issues across infrastructure and application layers.
  • Contribute to improving engineering productivity and developer experience.

Desired Qualifications

  • Experience with AI/ML infrastructure or GPU workloads.
  • Familiarity with distributed systems or compute platforms.
  • Exposure to platform engineering concepts.
  • Experience supporting 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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