AI Engineer, AI Platform Volta software

You’ll make Volta’s AI pipeline production-grade at enterprise scale: multi-tenant, observable, cost-efficient and configurable per customer without code changes. You’ll take parsing, matching and LLM orchestration services that already work and make them work for fifteen enterprise customers at once. You report to the Head of AI.

• Turn the Core into configuration: design the tenant configuration model (prompts, schemas, matching rules, thresholds, feature switches) so onboarding a new enterprise customer is configuration plus evaluation, not a code change

• Make multi-tenancy real: per-tenant data separation across services, storage, caches, knowledge bases and traces

• Scale the pipeline: Cloud Run services for ingestion, API, parsing and matching that absorb bursts of orders and large documents, with queues, idempotency and retries that don’t re-run the whole pipeline

• Make it observable: every LLM call traced and costed in Langfuse, trace IDs carried onto orders, alerts on stuck orders and quality regressions

• Keep cost per order under control: model choice per step, caching, batching, cost dashboards per customer

• Ship safely: replay and regression tests in CI for prompts and schemas, versioned prompts and configs, gradual rollout per tenant

• Make accuracy a release gate: plug per-customer evaluation harnesses into the delivery pipeline, with the data scientists

• Make it secure: treat prompts, model outputs, tools and data flows as untrusted attack surface, and enforce it with automated guardrails, least-privilege access and continuous red-teaming in CI/CD