Designing responsible, revenue-ready intelligence for commerce and customer platforms
- Shipped retrieval-augmented copilots that mediate billions of SKUs while respecting enterprise guardrails and privacy-by-design.
- Led multi-cloud MLOps initiatives that accelerated model iteration cycles from quarterly to weekly without sacrificing compliance.
- Mentored cross-functional squads adopting AI-first playbooks, raising experimentation velocity and measurable CX lift.
- Human-centered design – Shape AI experiences that respect context, governance, and responsible deployment.
- Operational resilience – Build observable, testable pipelines with continuous evaluation loops and rollback confidence.
- Commerce intelligence – Translate catalog, behavioral, and operational signals into adaptive merchandising and service journeys.
- Fei-Fei Li – ImageNet and her human-centered AI leadership motivate my bias toward inclusive, policy-aware solutions.
- Andrew Ng – Practical deep learning evangelism and Google Brain roots reinforce my "build useful tools, not hype" mantra.
- Geoffrey Hinton – Nobel-recognized neural network breakthroughs remind me to pair bold research with ethical stewardship.
- Yoshua Bengio – Deep learning trailblazing and global AI safety advocacy guide my experimentation frameworks.
- Demis Hassabis – DeepMind's science-driven approach informs my balance between frontier research and applied value.
- Architecting data pipelines that carry clean, observable signals from ingestion through model training, evaluation, and deployment.
- Building retrieval-augmented copilots that unlock catalogues, documentation, and analytics through natural language interactions.
- Embedding responsible AI guardrails—from dataset governance to human-in-the-loop feedback—so automation stays trustworthy.
- Coaching cross-functional teams on prompt engineering, embeddings, and MLOps, enabling them to deliver AI features with confidence.
- Modernising Adobe Commerce, BigCommerce, and Shopify implementations with AI-assisted merchandising, search, and support journeys.
- Unifying Akeneo, ERP, and customer signals into knowledge graphs that drive richer storytelling and targeted automation.
- Bringing AI observability and experimentation practices into delivery pipelines so every release is measurable and reliable.
- Investigating how retrieval, orchestration, and evaluation stacks combine to support multi-modal customer experiences.
- Mapping vector databases and agent frameworks to the realities of compliance-heavy environments.
- Studying agentic workflows that can automate QA and operational runbooks without losing human oversight.
- AI Fieldnotes – Real-world playbooks for RAG, safety, and evaluation drawn from enterprise pilots.
- Commerce Copilot Accelerator – Blueprint for merchandising and CX teams to adopt retrieval-augmented generation in under 90 days.
- Responsible AI Runbook – Governance framework aligning model lifecycle checkpoints with legal, compliance, and CX stakeholders.
AI, LLM & Automation
Vector & Knowledge Platforms
Data Platforms & Pipelines
Backend & Architecture
Commerce Platforms
Frontend & Experience
Ops, Cloud & Reliability
- 🔭 I love designing AI accelerators that elevate cross-functional teams—from intelligent product discovery to generative support flows.
- 🌱 Always sharpening my understanding of LLMOps, multi-agent orchestration, vector databases, and responsible AI governance.
- 👯 Open to collaborations on applied AI, digital experience platforms, and full-stack prototypes that push technology forward.
- 💬 Ask me about generative AI, solution architecture, Adobe Commerce, BigCommerce, Shopify, PHP, or Python.
If you're exploring how AI can elevate commerce experiences—or you just want to jam on architecture, automation, or digital strategy—drop me a line. I'm always happy to trade ideas, pair on prototypes, or mentor teams embracing intelligent experiences.