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Lead DevOps/MLOps Engineer

RAZOR

Key Facts

Remote From: 
Virginia (USA)
Category:  Lead Developer
Full time
Senior (5-10 years)
English

Roles & Responsibilities

  • Experience with production infrastructure at scale
  • Strong in practical DevOps execution and operational reliability
  • Knowledge of automation, observability, and deployment safety
  • Experience with distributed systems or GPU-oriented workloads

Requirements:

  • Improve CI/CD pipelines, deployment workflows, and release reliability
  • Standardize infrastructure and deployment patterns across environments
  • Improve observability through logging, metrics, tracing, dashboards, and monitoring
  • Support ML-oriented infrastructure including SageMaker workloads and GPU scaling patterns

Job description


We're looking for a strong DevOps engineer who can help scale and operationalize our infrastructure as the platform grows. This is not a pure platform-architecture role — the focus is CI/CD, infrastructure automation, deployment reliability, observability, and GPU-oriented workload scaling.
What You'll Own
  • Improve CI/CD pipelines, deployment workflows, and release reliability
  • Standardize infrastructure and deployment patterns across environments
  • Improve observability through logging, metrics, tracing, dashboards, and rollout monitoring
  • Partner closely with backend engineering on:
    • deployment strategies
    • infrastructure automation
    • environment consistency
    • migration workflows
    • possible Kubernetes migration efforts
  • Support ML-oriented infrastructure as a secondary responsibility:
    • SageMaker workloads
    • Ray clusters
    • GPU scaling patterns
    • distributed batch execution
    • autoscaling behavior
    • runtime/image management
    • artifact delivery/versioning
The Kind of Problems You'll Work On
  • Deployment safety and rollback strategies
  • Infrastructure consistency across environments
  • Release automation and environment promotion flows
  • Autoscaling and runtime stability
  • GPU workload orchestration and scaling efficiency
  • Operational tooling that reduces friction for engineering teams
Stack
  • AWS
  • Terraform
  • Docker
  • Kubernetes
  • CI/CD systems
  • SageMaker
  • Ray
  • GPU compute infrastructure
You'll Probably Do Well Here If
  • You've operated production infrastructure at meaningful scale
  • You're strong in practical DevOps execution and operational reliability
  • You care about automation, observability, and deployment safety
  • You're comfortable improving developer workflows and infrastructure tooling
  • You've worked with distributed systems or GPU-oriented workloads before
Ready to apply?
APPLY
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