/ Service
Governance is an operating model, not a checklist: who may use which capability, how access is granted and revoked, how usage and licence cost stay attributable, and where central control ends and team choice begins. Built into the paths teams already use, it accelerates adoption instead of blocking it.
/ What this covers
/ Evidence
Operating model · Platform standards
Designed the operating model that lets generative AI scale beyond pilots: identity lifecycle and SCIM provisioning, RBAC, a ServiceNow approval workflow, usage controls, licensing and cost governance, plus the ADRs and platform standards that guide technology selection across engineering teams.
Read the Enterprise AI Governance & Platform Strategy case study
ML platform · 100+ AWS accounts
Designed and implemented a secure ML and model-serving platform spanning 100+ AWS accounts. Open-source HuggingFace models are served through Amazon SageMaker with GPU-backed inference, automated model packaging pipelines and centralised governance controls — so teams keep their own accounts while security and lifecycle standards stay central.
/ Related services
ReAct agent architectures and grounded assistants, with tool access, guardrails and evaluation designed in.
Paved paths and internal platforms so AI applications start from a supportable, governed baseline.
Evaluation sets and retrieval tuning that make assistant accuracy and grounding measurable over time.
Governed agent access to internal systems through MCP servers and shared integration patterns.
/ Next step
Open to full-time Senior–Staff AI / agent platform roles (remote-friendly), as well as contract and consulting engagements. Response within two business days.