I help engineering teams maximize AI ROI.

Most teams do not have an AI tool problem. They have an AI enablement problem. I run custom training that turns AI spend into measurable output — through better workflows, stronger architecture decisions, and cost ownership tied to business value.

Book an AI & Cloud Assessment Call →

6 yrs At Amazon

AI ROI Usage To Output

Cost Ownership Spend Tied To Value

Production AI Built And Shipped

How I Help

Enablement is what makes AI spend pay back.

Companies are buying AI tools faster than they are teaching teams how to use them well. I help engineering leaders install the training, habits, metrics, and architecture guardrails that make AI adoption measurable instead of expensive.

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Custom AI Enablement Training

Hands-on workshops and bootcamps tailored to your stack, codebase, and use cases. Your team learns when to use AI, how to structure AI-assisted workflows, how to validate outputs, and how to apply AI to real engineering work.

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AI ROI & Usage Patterns

Turn AI from a line item into leverage. We map usage to outcomes like cycle time saved, incidents avoided, support load reduced, and features shipped, then define repeatable patterns your team can keep using.

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AI Cost Ownership & Culture

Build the cost discipline most companies skip: who owns AI spend, how model decisions get made, when usage escalates, and how teams talk about cost without slowing down adoption.

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AI Cost & Architecture Reviews

Review your AI stack for wasted spend, risky patterns, and better model choices. Compare proprietary, open source, hosted, and self-managed paths with scale, latency, ownership, and ROI in mind.

Book an AI & Cloud Assessment Call →

Why Me

I bridge engineering enablement, AI architecture, and cost discipline.

I spent 6 years at Amazon — first as a Senior Technical Account Manager, working with enterprise teams on architecture reviews, cost risks, operating mechanisms, and action plans.

Then I moved into engineering — building LLM platforms, RAG systems, and agentic AI workflows used in production.

That combination matters because AI training only works when it matches production reality: the models, code paths, budgets, workflows, and teams actually doing the work.

Sr. Technical Account Manager

Amazon Web Services

Reviewed enterprise architectures, identified cost risks, delivered action plans, and helped teams build operating rhythms around spend and reliability.

Software Development Engineer

Amazon

Built LLM platforms, RAG pipelines, and production AI systems. Learned the tradeoffs that only show up when teams have to ship and own the system.

Founder — CloudYeti

AI Enablement & Cost Ownership

Now I help engineering teams adopt AI through custom training, cost reviews, architecture guidance, and usage patterns that tie spend to output.