About Tribe AI
At Tribe, we’re on a mission to help enterprises rearchitect their business with AI. Today, every large enterprise wants to transform its business with AI, but they often lack the capabilities to do so. At Tribe, this gap is our opportunity.
About the Role
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.
Key Responsibilities
Client Environment Deployment
- 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.
Technical Ownership
- 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.
Client Stakeholder Management
- 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.
About You
- 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
Why Join Us
- 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.
