[Hiring] AI Systems Architect @TubeScience-Labs

Role Description

TubeScience is hiring an AI Systems Architect to serve as the technical lead for the infrastructure at the core of our production AI platform. You'll own the technical direction for how these systems are designed, working closely with product and engineering across the org, and then you'll build them. This is a deeply hands-on, greenfield role.

• Own the infrastructure core: Set the technical strategy for the media pipelines, LLM routing, distributed data systems, and observability tooling that the entire platform runs on — and architect them from first principles.
• Design AI agent systems: Build AI agents and multi-agent systems from the ground up — orchestration, tool-calling, MCP servers, A2A dispatch, and the permissions and observability infrastructure that makes them reliable in production.
• Go deep on distributed systems: Push into the hard parts — data flow, fault boundaries, consistency trade-offs, performance under load — to build infrastructure that holds up when it matters.
• Raise the bar on reliability: Design for observability from day one — tracing, metrics, structured logging — and build the quality systems that let a fast-moving team move without breaking things.
• Shape the roadmap: Bring an engineering point of view to platform strategy — what we should build, in what order, and why.

Qualifications

• Have set technical direction across teams, and can point to specific systems you architected and operated in production, and the moments where you brought people along.
• Are deeply expert in distributed systems including the layers below the framework — data flow, fault tolerance, consistency, and performance under real load.
• Design AI agents and agentic systems: orchestration, tool-calling, multi-agent coordination, and the infrastructure that makes them observable and production-ready.
• Have built large-scale platforms from scratch — making the foundational decisions and living with them — not just joined a mature codebase.
• Design clean, durable APIs across REST, gRPC, event-driven, and schema-based systems, and are fluent with MCP, A2A, schema registries, and tool-calling normalization.
• Know media pipelines deeply — transcoding, ffmpeg-class tooling, cloud storage, large-object processing — with strong hands-on depth across PostgreSQL, OpenSearch, Redis, Docker, Kubernetes, and Terraform or Pulumi.
• Are the person teams want in the room when a hard architectural call has to be made.

Requirements

• Production experience at the intersection of AI and infrastructure — LLM APIs, multi-provider routing and abstraction, schema normalization, structured output, semantic caching, and prompt engineering at the infrastructure layer.
• Experience building the plumbing that makes agentic AI systems reliable and observable at scale — in production, not just in demos.
• A track record of seeing a multi-quarter technical strategy through from proposal to outcome.

Benefits

• Competitive compensation.
• Comprehensive benefits.
• Generous access to frontier and open-weight models for day-to-day work, prototyping, and evals.
• Opportunity to work on hard problems, with expert users from day one, and the room to take an idea from prototype to shipped product.