Ship one real model-production workload.
An end-to-end delivery motion for a priority workload: data to training, post-training, evaluation, inference, and operating handoff inside the client environment.
Target workload, data boundary, and platform owners are identified.
Eval and release board tied to model quality and serving SLOs.
Production launch, blocked launch, or explicit extension backlog.
Business workload owner plus AI or platform technical owner.
The work inside Workload Delivery.
Data and ingestion
Build the data boundary, curation path, sharding, tokenization, lineage, quality checks, and throughput profile required by the target workload.
Training or fine-tuning
Run pretraining, continued pretraining, SFT, or fine-tuning with validated parallelism, checkpointing, and convergence instrumentation.
Post-training
Implement preference optimization, RLHF/DPO-style loops, reward modeling, red-team data loops, or domain-specific alignment where the use case requires it.
Evaluation gate
Create offline and domain evals, regression suites, acceptance thresholds, score dashboards, and release gates tied to the business objective.
Serving and inference
Optimize quantization, batching, KV-cache, parallel inference, autoscaling policy, and latency/throughput tradeoffs for production use.
Operational handoff
Turn the delivered workload into a reusable internal capability: runbooks, dashboards, ownership model, and extension plan.
- Reproducible model-training or fine-tuning pipeline
- Post-training workflow where required
- Evaluation harness and release criteria
- Production serving endpoint or deployable serving package
- Cost-per-run and cost-per-token reporting
- Operating handoff package and extension backlog