Google

Scale Fortune 500 enterprises, government agencies, and high-growth startups by architecting and deploying AI solutions that drive measurable business transformation. Deliver engagements resulting in 130%+ revenue growth, 8-figure cost savings, and AI-enabled process automation reducing operational cycle times by 81%. Lead AI adoption programs that take early-stage startups from prototype to market-leading platforms, and transform legacy enterprises into AI-driven market leaders.

Design and implement end-to-end AI ecosystems leveraging Google Vertex AI, TensorFlow Extended (TFX), and TPU v5e infrastructure. Integrate large language models (LLMs), multimodal AI pipelines, and retrieval-augmented generation (RAG) workflows tailored to domain-specific use cases. Engineer petabyte-scale data pipelines with BigQuery and Dataflow, embedding vector search for real-time semantic retrieval.

Deploy production-grade AI using GKE and Kubeflow Pipelines, enabling CI/CD-driven MLOps with automated model monitoring, drift detection, and rapid retraining. Optimize inference latency by up to 60% through TensorRT acceleration, mixed-precision computation, and graph pruning. Deliver cross-domain AI applications spanning healthcare diagnostics, real-time financial fraud detection, industrial automation, and secure defense systems, serving millions of end users worldwide.

AI Consulting Strategist – Google AI

NVIDIA

Developed GTM strategy for enterprise deployment of NVIDIA’s LLM inference stack, targeting financial services, logistics, and healthcare sectors. Led internal pilot program to monetize edge AI deployment tools for multi-modal sensor data, resulting in $60M+ in downstream enterprise contracts. Designed pricing model for inference-as-a-service API bundles; used in 4 Fortune 500 client rollouts. Partnered with engineering teams to productize low-latency LLM pipelines on A100/H100 architecture for high-frequency decision environments. Presented enterprise AI monetization playbook at NVIDIA Executive Forum; used to inform FY23 enterprise roadmap. Built economic viability framework for internal AI tooling, prioritizing initiatives with >4x projected ROI.

AI Commercialization Strategist – Enterprise Solutions

Apple

Led monetization design for Vision Pro’s AI-native features, including intent prediction, gaze tracking analytics, and adaptive content surfacing. Worked cross-functionally with ML engineers and product to translate LLM-based user modeling into paid in-app personalization APIs. Built dynamic pricing framework for AI-driven developer tools on VisionOS, projected to generate $180M+ in ARR by 2025. Defined core metrics for AI feature adoption, driving a 3.7x increase in developer conversion across beta cohorts. Presented strategic roadmap for AI revenue growth to executive leadership; approved for rollout across 4 product verticals. Contributed to Apple’s internal thesis on “Ambient AI Monetization,” later used in fiscal year investment planning

AI Monetization Strategist – Vision Pro Team

I build and monetize AI systems. At Apple, I shaped the Vision Pro AI revenue strategy. At NVIDIA, I helped commercialize LLM infrastructure for enterprise deployment. I specialize in turning ML capabilities into scalable business models, especially in environments where speed, monetization, and leverage matter more than polish. I’ve advised AI startups, contributed to strategic go-to-market plans, and built pricing frameworks now used by Fortune 500 teams. My background is technical, but my advantage is translating that into revenue. I’m open to high-leverage collaborations, especially where AI meets distribution, B2B tooling, or capital.

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