Senior DevOps Engineer/SRE

The FlexAI Compute Infrastructure Platform provides an "end-to-end AI compute layer" for running and managing workloads across any cloud, any GPU, and any deployment model (public, hybrid, or on-prem). It brings together "1-click simplicity" for users with "enterprise-grade orchestration, security, and automation" under the hood.

FlexAI is looking for a Senior DevOps / SRE Engineer to build and operate the infrastructure powering our AI and PaaS platform.

• →Build and maintain infrastructure for our 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 SLIs, SLOs, and error budgets
• Improve system reliability, availability, and performance
• Participate in on-call rotations, incident response, and postmortems

• Build and improve CI/CD 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, Grafana (metrics, logs, traces)
• Monitor systems and troubleshoot performance issues (latency, throughput, cost)

• Work closely with developers, platform, and AI teams to support production systems
• Help debug issues across infrastructure and application layers
• Contribute to improving engineering productivity and developer experience

What You’ll Need to Be Successful

• 4+ years of experience in DevOps, SRE, or Infrastructure Engineering
• Experience operating production systems at scale
• Hands-on experience with:
• • Kubernetes & containers
  • Infrastructure as Code (Pulumi, Terraform, etc.)
  • Cloud or hybrid environments (AWS, GCP, Azure, or on-prem)
  • Observability tools (Prometheus, Grafana, OpenTelemetry)
• Experience with CI/CD systems and automation
• Proficiency in Python, Go, or Bash
• Strong debugging and problem-solving skills
• Familiarity with SLOs and reliability practices
• Experience working in startup or fast-paced environments
• Comfortable leveraging AI coding tools and agents

• 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

• Work on cutting-edge AI infrastructure
• Build systems that power developers and enterprises
• High ownership, fast execution, real impact
• Collaborative, high-caliber team