applied ai · every layer of the stack
The gap between AI ambition and AI reality isn't a technology problem. It's a talent problem.
Whether it's the foundations of the data lake, the pipes that feed it, or the agents deployed across your procurement process. We place the people who build it.
the stack — reporting
production
[04 infra]
the stack
We hire across all four, from the engineers holding it up to the executives who own it.
01
AI deployment
Ships it into production
02
AI platforms
Serves and runs the models
03
AI data
Feeds it and retrieves from it
04
AI infrastructure
The compute underneath
seniority
Chief · VP
Head of · Director
Staff · Principal
Senior
The gap we close
We place the ones who ship them.
@@ pilot → production @@
Coverage
model: ai-deployment
v1 · prod
class
Senior engineer → Chief AI Officer
system
Takes the model into the business. Owns what ships, the evals that gate it, and the fallback when it breaks. Sits with the CFO in the morning, ships production code in the afternoon.
tools
[agents, evals, integration, change_mgmt]
also shipped as
AI Deployment Architect · Forward Deployed Engineer · Head of AI Delivery · Chief AI Officer
model: ai-platforms
v1 · prod
class
Senior engineer → VP AI Platform
system
Makes deployment repeatable. Serving, inference, orchestration, observability. The reason the second model ships faster than the first, and the reason the first one stays up.
tools
[serving, inference, orchestration, observability]
also shipped as
ML Platform Engineer · MLOps Lead · Head of ML Platform · VP AI Platform
model: ai-data
v1 · prod
class
Senior engineer → VP AI Data
system
Feeds the model and gets the answer back out. Pipelines, retrieval, context. Decides what the model actually sees, which decides whether anyone trusts it.
tools
[pipelines, retrieval, context, data_quality]
also shipped as
AI Data Engineer · Retrieval Engineer · Head of AI Data · VP Data
model: ai-infrastructure
v1 · prod
class
Senior engineer → VP AI Compute
system
The compute underneath. GPU clusters, scheduling, training fabric, capacity. Building the data centre is someone else's job. Making the GPUs useful is this one.
tools
[gpu_cluster, scheduling, training_fabric, capacity]
also shipped as
GPU Platform Engineer · Cluster Engineer · Head of AI Compute · VP Compute
The Firm
Hundreds
AI roles closed
<30 days
Average time to hire
London → Singapore
And everywhere in between
5 yrs
Building AI teams
The POC graveyard is full. We place the people who empty it.