/ Service
When every team solves deployment, secrets, tool access and evaluation independently, each application arrives with a different security posture and a different operational shape. A platform turns those solved problems into defaults — a developer harness on the delivery side, governed model serving on the runtime side.
/ What this covers
/ Evidence
Internal platform · Paved path
I built the Dev Harness: an internal paved path for enterprise AI delivery. It packages reusable application templates, deployment automation, MCP and tool-integration patterns, security guardrails, evaluation workflows, observability and CI/CD so an AI application starts from a supportable baseline instead of an empty repository.
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.
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.
The operating model — identity, approvals, usage and cost controls — that lets AI scale past pilots.
/ 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.