How a YC-backed AI startup hired AI engineers in two weeks, then scaled to five
How a YC-backed AI startup hired AI engineers through Lyra: the first started within two weeks, a second followed days later, and five embedded engineers now ship its platform.
A YC-backed AI startup selling to large enterprises needed AI engineers within two weeks. Lyra placed its first forward deployed engineer inside that window, added a second days later, and grew the embedded team to five engineers in five months, working across the company's ingestion pipeline, evaluation harness and core platform.
At a glance
- Client: seed-stage, YC-backed AI startup; enterprise compliance software
- Need: senior AI and platform engineers, fast
- Team: 1 engineer in week one, 2 within weeks, 5 by month five
- Stack: Python, TypeScript, gRPC, Kubernetes, Argo CD, Grafana, Langfuse
The problem
The company had enterprise customers waiting and a roadmap well ahead of its engineering headcount. It needed someone productive within one to two weeks, which ruled out a normal hiring process.
The codebase was moving fast and lightly documented. There was no local environment and no mock data, so any new engineer had to build their own way in before they could ship.
What Lyra's engineers built
Platform foundations. An API gateway, a separate identity service and typed gRPC contracts between services, deployed on Kubernetes with Argo CD. Observability through Grafana and Langfuse, with timing on every stage of the document ingestion pipeline.
The AI pipeline. Obligation extraction from policy documents, relationship discovery between policies, and clustering. A remediation agent that proposes document edits and reconciles overlapping edits before the final version, plus a review workflow where expert feedback becomes a proposed policy update that a person approves.
Evaluation. A test harness with golden datasets, configurable judge models and a deliberate no-violation control document, so every model or prompt change is measured before it ships.
Product. User invitations and activation, audit logging, bulk actions and a graph view of how obligations connect.
Results
- First engineer productive within the two-week window, with a second requested days later.
- Five embedded engineers by month five, one of them now the client's technical lead on the account.
- Ingestion 3.5x to 6.6x faster depending on document type.
- Clustering coverage from 30% to 90%.
- Eval accuracy up on the golden set, with fewer false positives at the same recall.
Why embedded AI engineers instead of staff augmentation
Traditional staff augmentation fills a seat with whoever is available and leaves the management to you. This client needed engineers who could find their own way into an undocumented codebase and own whole systems: the eval harness, the gateway, the ingestion timing. Lyra's engineers are full-time Lyra employees working on one client at a time, vetted (fewer than 3% of applicants get in) and AI-native, so they operate like founding engineers rather than contractors working through a ticket queue.
Read more: What is a forward deployed engineer?
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