Watts to Workers.
Source, train, and place the operating team that runs your cluster. Before go-live, not after.
A provisioned cluster without an operating team is just a lit rack nobody can run. Watts to Workers sources candidates through Ai4Jobs, trains them through OpenSesame, and plans the staffing model through Maker Year. The team that runs your platform is in place before the first workload lands.
Why this matters
Hardware lands on time, seats sit empty.
Cluster operators are shipping racks faster than they can hire. Specialized sovereign AI roles (platform engineers, NOC analysts, tenant-support engineers, compliance leads) are new enough that no training program, no recruiter desk, and no community college curriculum has caught up. The result is a pattern we see on every buildout: the hardware lands on time, the software deploys cleanly, and then the program stalls at operational readiness because the seats are empty.
Watts to Workers solves that in three pieces. Ai4Jobs sources candidates against the specific roles a sovereign AI platform needs. OpenSesame trains them on the hardware, software, and operating model you'll actually run. Maker Year plans the workforce pipeline with your local community colleges, state workforce boards, and partner universities, so the supply of operators compounds instead of running dry.
How it works
Three pillars, one program.
Sourcing (Ai4Jobs)
Ai4Jobs is the sourcing engine that pulls candidates from job boards, workforce programs, and ThisWayGlobal's own network into a pipeline matched against the six canonical Watts to Workers roles. Every sourced candidate is pre-qualified for a sovereign AI platform context: data residency comfort, shift tolerance, and the baseline technical skills the platform assumes.
Training (OpenSesame)
OpenSesame is the training program. Structured curriculum against the specific hardware, software, and operating model your platform runs. Candidates complete it before they arrive on site, so your ops team starts productive on day one rather than spending their first quarter learning the stack.
Resource planning (Maker Year)
Maker Year is the workforce partnership layer. We build a staffing pipeline with your local community colleges, state workforce boards, and partner universities so the supply of operators keeps pace with platform expansion. Long-term durability, not a one-time hire.
What you get
Staffing delivered, not staffing outsourced.
An operating team in place before go-live.
Hired, trained, and on-site before first workload.
Six-role coverage out of the box.
Platform Engineer, NOC (Network Operations Center) Analyst, Tenant Support Engineer, Sully Administrator, Billing and Metering Operator, and Security and Compliance Lead.
A staffing model sized to your megawatts.
Headcount, shift structure, and compensation bands built from real sovereign AI platform data.
Training paths certified on your stack.
OpenSesame curriculum tuned to the silicon, software, and operating model you run.
A durable candidate pipeline.
Maker Year keeps the pipeline filled as you expand to the next rack, pod, or site.
Data back into the platform.
Every engagement feeds Ai4Jobs and the compensation benchmarks, sharpening the next one.
Offering scope
Start with sizing, scale from there.
Proof
Named customers, running programs.
- Operating team in place before the first workload, not scrambled for after commissioning.
- Six canonical roles sourced, trained, and placed through one program.
- Candidate pipeline that compounds through local workforce partnerships, not a one-time hire.
- Staffing model sized in 10 business days, included in Build Capacity engagements.
Staff before go-live, not after.
Tell us about your platform and we'll send you a sized staffing model in 10 business days.