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Tribe AI

Forward Deployed Infrastructure Engineer

Tribe AI

Infrastructure Engineer deploying AI systems in client infrastructures. Navigating enterprise constraints and debugging production systems in a variety of environments.

Posted 5/6/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSenior💰 $200,000 - $300,000 per yearWebsite

Tech Stack

Tools & technologies
AWSAzureCloudDNSGoogle Cloud PlatformKubernetes

About the role

Key responsibilities & impact
  • Get AI systems running inside client infrastructure — cloud, containers, CI/CD.
  • Navigate enterprise constraints: production readiness reviews, governance gates.
  • Debug what breaks in production: networking, DNS, connection pooling.
  • Set up observability, cost controls, and deployment pipelines.
  • Own the technical relationship with the client's IT, Infrastructure, and DevOps counterparts.
  • Get things done inside organizations where you control nothing and depend on everyone.

Requirements

What you’ll need
  • Expert-level in at least one cloud platform — AWS, GCP, or Azure.
  • Strong hands-on experience with Kubernetes in production environments.
  • Productionized data science outputs, deployed ML models, or run AI applications at scale.
  • Deep production debugging experience: networking, DNS, latency, connection pooling.
  • Managed technical relationships directly with client IT or DevOps counterparts.
  • Background reads: production engineer, systems engineer, SRE, or platform engineer.

Benefits

Comp & perks
  • Impact: Ship AI systems that don’t just demo well but run at scale in Fortune 500 enterprises.
  • Growth: Stay hands-on with cutting-edge frameworks while developing field-tested instincts.
  • Variety: Solve problems across industries, from finance to healthcare to defense.
  • Culture: Work in a team that prizes resilience, creativity, and winning over process.
  • Trajectory: Build both your technical and consulting muscles in one of the most demanding roles in AI delivery.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools
cloud computingKubernetesAI applicationsML modelsproduction debuggingnetworkingDNSconnection poolingCI/CDobservability
Soft Skills
relationship managementorganizational skillsproblem-solvingcollaborationcommunication