AI Systems Engineer

About the role

ABOUT VAILENT Vailent is the AI infrastructure for the materials industry - chemicals, polymers, elastomers, rubber. The companies in this space run on a mess of CRMs, ERPs, point tools, and flat files. We're replacing all of that with one system that turns every interaction, transaction, and physical asset into usable commercial data. Materials are the foundation of the physical economy: they're in everything. Every product humans build, ship, eat, wear, or drive starts here. But the industry is still massively under-instrumented, running on fragmented tools and the institutional knowledge of people who've been doing it for decades. At Vailent, we're building the infrastructure that will transform this industry for the next century, capturing multi-modal industry context across both software and hardware. About the Role A full-stack platform engineer who can run a multi-app B2B platform end to end - by directing fleets of AI agents and verifying everything in the real environment. You'll own the whole stack: cloud infrastructure, backend, frontend, data, and deep enterprise-ERP integration. The job isn't writing code with AI; it's operating it - decompose, fan out, verify adversarially, ship. One seat doing what's normally three or four. We run a B2B platform spanning roughly ten applications on a shared cloud backbone, with deep integration into customers' enterprise systems (SAP/ERP). This role owns it end to end - from the Terraform and IAM underneath to the React components on top, and the SAP RFC calls in between. The differentiator isn't typing speed. It's the ability to hold an entire platform in your head and conduct AI agents through it without dropping correctness - shipping across many repositories at once while keeping the architecture coherent. AI orchestration here is not a productivity add-on; it's the core multiplier that makes the scope possible. We hire for that fluency, and for the discipline that makes it safe. What You'll Do Own the platform end to end. Multiple applications plus shared SDKs on a single cloud backbone - React/TypeScript front ends, FastAPI/Python services, the Terraform/IAM/ECS infrastructure underneath, and a shared design system. Stand up infrastructure and environments from scratch. New services, cloud accounts, tenants, connectors, data syncs, migrations (including cross-region) - provisioned and proven, never just stood up and assumed. Direct fleets of coding agents. Decompose a cross-repo change into disjoint tasks, fan them out to parallel agents in isolated worktrees, run adversarial multi-reviewer passes, then reconcile the results. Integrate with enterprise systems at depth. SAP/ERP integration via RFC/BAPI - reading and where necessary authoring ABAP, reverse-engineering business rules, handling sales-order and customer-master flows, currency/unit/sales-area mapping, and idempotent event sync. Architect multi-tenant data. Postgres row-level security as the tenant-isolation core, JSONB-backed tenant-extensible capability platforms (custom fields, validation, masking), careful migrations, and a graph database where it fits. Ship at volume without losing coherence. Multiple PRs across multiple repos in a working session, CI green, deployed and verified - while keeping the design clean. Author the thinking, not just the code. Specs, design docs, discovery-question sets, and runbooks that let work be understood and resumed by others. Build the tooling that makes AI effective here. Per-codebase navigation maps, documentation indexes, guard hooks, and custom skills - invest in making agents good at this codebase, then reap it on every task after. Automate yourself forward. Treat every repeated task as a bug to be fixed. When a workflow recurs, capture it as a reusable Claude skill, hook, or slash command so the next run - yours or a teammate's - is one step instead of ten. Review like an adversary, deploy like a surgeon. Catch the regression the happy path missed, separate "it renders" from "the data is correct," refute false blockers, and touch shared state only with a reason and a green light. How We Work Hire for the disposition. The stack is learnable; this isn't. These principles are non-negotiable, because at this volume they're what keep the work correct. If you don't already work this way, the throughput becomes a liability instead of an asset. 01 - Prove it in the real environment. "Done" means demonstrated, not asserted. A green badge over $0 / insufficient data is a failure. subrc=0 means nothing until the record reads back. The data wins, never the badge. 02 - Never guess. Verify what's knowable in the code; ask about what's a genuine product decision; assume nothing in between. Confident fiction is worse than an honest "I don't know yet." 03 - Diagnose before you touch. "Look into it" means read-only until told to fix - especially on anything live. Root cause and a proposed fix come first; the change waits for an explicit go. Production is sacred. 04 -

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