Stay close until the capability is internal.
Senior ML systems engineers embedded with the client team to keep private AI factory workloads running, improving, and transferring into internal ownership.
Strategic cluster with multiple workload teams and shared capacity.
Scheduler policy readout with utilization, queue, and ownership signals.
Internal team controls run support, triage, and operating backlog.
Executive sponsor, platform lead, and model-production owners.
Resident engineering is where utilization becomes a managed system.
GPU capacity scheduler
Allocation is scattered. Free GPUs exist, but not as one schedulable block.
Representative managed partition. Metrics derive from the scheduler state above.
The work inside Resident Engineering.
Operating cadence
Run technical reviews across training, inference, scheduler policy, incidents, utilization, backlog, and business deadlines.
Run support
Support high-value training and serving workloads through launch, monitoring, failure response, checkpoint recovery, and performance tuning.
Capacity economics
Manage training-versus-inference allocation, queue health, fragmentation, preemption policy, utilization, and cost-per-token/cost-per-run visibility.
Platform maturity
Convert repeated field problems into reusable templates, guardrails, dashboards, runbooks, and platform features the internal team keeps.
Team enablement
Teach the client engineers how to debug the stack: fabric, storage, scheduler, training loops, evals, and serving SLOs.
Roadmap and governance
Turn the cluster into a managed portfolio of workloads with priorities, acceptance criteria, and an escalation model.
- Resident engineering cadence and operating report
- Utilization, queue, and economics dashboards
- Production workload support and incident response
- Reusable recipes, templates, and runbooks
- Client team training and ownership transfer
- Roadmap for private AI factory operations