Staff Engineer (AI-Native Delivery)

About this 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 — Copy what works. If working examples already solve a problem, read the proven pattern and adapt it. Don't
invent a fresh approach and burn an afternoon proving it wrong.
05 — Enhance in place, never fork. Generalize the existing path — add an optional parameter where today is the
degenerate case — rather than shipping a parallel reimplementation. Design the capability; a single customer is the
validating example, not the spec.
06 — Risk isn't size. Bigger isn't worse; riskier is. Risk is load-bearing code modified × silent-failure potential × blast
radius. A large additive change can be safer than a one-line edit to a hot path.
07 — Build to scale — or name the debt. Ship the agreed slice now, but flag anything that won't scale as explicit,
revisit-able debt. Hardcoded shortcuts are fine only when chosen out loud, never smuggled in.
08 — Own the correction. Verify findings adversarially — a second pass whose…