Forward Deployed Infrastructure Engineer

Tribe AI

2d

About The Position

Every engagement moves through three environments: local, Tribe-controlled, and the client's own system. The first two are practice. The third is where AI actually ships — and it's where everything gets hard. You own that third environment. 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.

Requirements

• Expert-level in at least one cloud platform — AWS, GCP, or Azure. Cloud skills transfer; depth in one is enough
• 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
• Deep production debugging experience: networking, DNS, latency, connection pooling, systems that break in ways no diagram predicted
• You've managed technical relationships directly with client IT or DevOps counterparts and know how to get things done inside organizations you don't control
• Your background reads: production engineer, systems engineer, SRE, or platform engineer with client-facing or embedded delivery experience

Responsibilities

• Get AI systems running inside client infrastructure — cloud, containers, CI/CD, networking, observability — under the client's rules, not ours.
• Navigate enterprise constraints: production readiness reviews, governance gates, on-prem requirements, and siloed IT teams that each own a different piece.
• Catch in environments one and two what won't survive the client's production environment — before anyone finds out the hard way.
• Debug what breaks in production: networking, DNS, connection pooling, latency, and scaling under real traffic.
• Set up observability, cost controls, and deployment pipelines the engagement team can actually operate after you've moved on.
• Make infrastructure-as-code decisions that hold up under client governance — Terraform, Pulumi, or equivalent.
• Own the technical relationship with the client's IT, Infrastructure, and DevOps counterparts directly — too tight and too technical to route through a PM.
• Navigate access control, approval chains, and institutional knowledge that lives in people's heads.
• Get things done inside organizations where you control nothing and depend on everyone.

Benefits

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed