adp-enablement

Operate GPU research workloads on managed Kubernetes (Lambda-class clouds): GPU sharing (MIG/MPS/time-slicing), KEDA autoscaling on DCGM metrics, cost chargeback, researcher tenancy vending, GitOps bootstrap, CIS-derived security baseline with shared-responsibility filter, and GPU workload troubleshooting.

Turn research and interviews into publishable deliverables: evidence-first research briefs and interview-based case studies.

Test and release models with evidence: model-level eval harnesses (synthetic ground truth, LLM-as-judge), GPU inference benchmarking (TTFT, tokens/sec, cost), release gates (explainability, fairness, adversarial robustness), rollout strategies (shadow/canary/bandit), 4-monitor drift stack, and GenAI red-teaming.

Operate AI at work: model selection/routing, work-shape triage (chat/agent/team/nothing), agent output verification, cost/ownership/tool governance, and harness engineering (instructions, memory, handoffs).

Make research data collection trustworthy without gatekeeping: ODCS data contracts with CI enforcement, dataset QoS/SLOs, data-product reviews (DAUTNIVS), right-sized governance with steward roles and certification, agent-consumable dataset catalogs, and lakehouse storage architecture for training data.

Engineering career coaching: senior/staff level-up behaviors, org signal reading, behavioral interview prep, AI-era positioning.

Run model creation and training as a product: training pipeline architecture (Argo/Kubeflow, trigger taxonomy), MLflow experiment/registry standards, distributed-training topology selection (Ray/Dask/Spark), Ray on K8s operations, notebook-to-production golden path, and fine-tuning strategy (prompt vs RAG vs PEFT).

Thirty-five reusable workflows for model routing, personal productivity, code comprehension, knowledge systems, agent evaluation, consumer AI strategy, and Office documents.

Marketplace

Azure DevOps CI/CD and full-SDLC assessment: CLI-first discovery (az + azure-devops extension), Wiki-ready Mermaid documentation, governed pipeline architecture with 30/60/90 roadmaps, adversarial PR review (Advocate / Skeptic / Judge), and chapter-indexed domain reasoning across twenty-five comprehensive engineering book guides.