Industry Impact
0 → 1
Platform delivery at scale
Architected Vertex AI Studio’s quota tiering foundation, unlocking self-service access and differentiated service levels.
Leadership storyHi, I'm Vincent
Tech Lead & Architect
I design and lead production AI platforms where safety, throughput, and reliability are first-class constraints. Creator of the Aether Platform.
Selected Impact
Evidence before adjectives.
A snapshot of how I operate across industry delivery, hands-on platform engineering, and technical leadership.
Platform Scope
4 components
Safe GenAI, end to end
Built Aether as a locally validated reference stack connecting content safety, traffic governance, inference, and observability.
Read Aether case studyTechnical Leadership
Design → Delivery
Decisions teams can execute
Turn ambiguous platform goals into explicit trade-offs, durable architecture, and operating guardrails teams can ship.
Read system designsFeatured Projects
Three systems that show how I approach safety, traffic governance, and production-scale AI infrastructure.
Aether
Reference ImplementationPlatform
An independently built, production-oriented Safe GenAI reference implementation integrating content safety, traffic governance, ML inference, and observability.
Role:Architecture and implementation
Ownership:Independent
Validation:Local integration testing
Sentinel
Reference ImplementationAI SecurityFormer Public API
An independently built AI supervision reference implementation for enforcing safety, compliance, and quality policies around LLM applications.
Role:Architecture and implementation
Ownership:Independent
Validation:Local functional testing; formerly served via RapidAPI
Atlas
Reference ImplementationLLM GatewayDistributed Systems
An independently built, production-oriented LLM traffic and quota gateway using Redis, FastAPI, and Prometheus, validated through a local automated test suite.
Role:Architecture and implementation
Ownership:Independent
Validation:46 automated tests passed locally
System Design
Deep dives into the architecture of agentic systems.
v1.0 • Stable
Agentic Orchestration Patterns
Designing the operating system for AI: Durable execution, memory kernels, and cognitive architectures.
Read Spec
v1.0 • Stable
Evaluation-Driven Development (EDD)
Moving from 'Vibe Checks' to metrics. A comprehensive framework for testing stochastic AI systems.
Read Spec
v1.0 • Stable
The Architecture of a Semantic Firewall
How to design a low-latency, fail-closed security layer for autonomous agents. Lessons from building Guardian.
Read Spec
Latest Insights
Writing about engineering, leadership, and AI.
• 12 min read
From Batch Size to TPU Topology: A Capacity Equation for ML Serving
A practical equation for turning measured batch throughput, latency limits, replica count, and TPU topology into an ML serving capacity estimate.
Read Article • 13 min read
GenAI Capacity Is a Product, Not Just an Accelerator Pool
Lessons from working across ML infrastructure and GenAI serving: reliable capacity requires a product contract across accelerators, entitlements, admission control, scheduling, and operations.
Read Article • 7 min read
Engineering Leadership: My User Manual
A concise user manual that explains how I collaborate, make technical decisions, and communicate on cross-functional engineering projects.
Read Article