Tribe AI
Forward Deployed Infrastructure Engineer
United States
Sponsorship not specifiedDetected 77 days ago
AWSGCPCloud PlatformsKubernetesTerraformCI/CDDevOpsSite Reliability EngineeringPlatform EngineeringMachine LearningData ScienceStakeholder ManagementSystems EngineeringDNS
About the role
- The third is where AI actually ships - and it's where everything gets hard.
- You're the person who gets the system running inside a heavily governed financial services tenant, a consumer-facing platform at massive scale, or an enterprise with four ticket systems and no single person who controls all the pieces.
- You also show up in environments one and two - lightweight but essential - catching the architecture decisions early that will be expensive to undo once you're in production.
Responsibilities
- Navigate enterprise constraints: production readiness reviews, governance gates, on-prem requirements, and siloed IT teams that each own a different piece.
- Own the technical relationship with the client's IT, Infrastructure, and DevOps counterparts directly - too tight and too technical to route through a PM.
- Growth: Stay hands-on with cutting-edge frameworks while developing field-tested instincts.
- Trajectory: Build both your technical and consulting muscles in one of the most demanding roles in AI delivery.
Requirements
- Strong hands-on experience with Kubernetes in production environments - central to how Tribe deploys
- You've productionized data science outputs, deployed ML models, or run AI applications at scale - you know what models demand from infrastructure
Skills
- Make infrastructure-as-code decisions that hold up under client governance - Terraform, Pulumi, or equivalent.
- Navigate access control, approval chains, and institutional knowledge that lives in people's heads.
Benefits
- Variety: Solve problems across industries, from finance to healthcare to defense.
Company info
- Work in a team that prizes resilience, creativity, and winning over process.
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This listing is sourced directly from Tribe AI's careers page and normalized into a canonical job model.