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Dylan Martin

Dylan Martin is an AI Tooling Developer at Amazon (AWS) based in Seattle, WA. He completed his Bachelors in Computer Science and his MSc in Applied AI and Data Science. 

Dylans work has generated 7 figure revenue, been shown at AWS re:Invent to more than 50,000 attendees, on stage at HIMSS 2024/2025 and online across AWS Technical Blogs and Official YouTube Channel. 

A bit about me

Connect with me!

  • GitHub
  • LinkedIn
  • Amazon

I grew up in Boston

NYC

before moving to

In NYC, I:

- Worked as a Solutions Architect supporting IBM WatsonX model training on AWS

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- Generated 7 figure revenue from a healthcare application I built

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- Became an AI/ML Specialist SA

and after a few years

SEA

I relocated to

In SEA, I:

- Pivoted to developing AI Generative Tooling

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- Was promoted to L5

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- Solo built a tech enablement generative tooling suite to automate the work of an entire organization. 

Putting it all together....

- 5 YoE as a SWE and SA

- Bachelors in CS

- Masters in Applied AI/DataSci

- 5 AWS Blogs Published 

- 12 Internal AI Tools Built

- 8 AWS Certifications 

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Engineering Stack

A collection of AI frameworks, tools, and APIs I specialize in for building generative AI-driven applications, automation, intelligent systems, and large-scale machine learning solutions

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Some recent projects include:

A machine learning powered pricing optimization tool for Seattle Airbnb properties. Built on XGBoost and trained with authentic Airbnb market data, the model analyzes property attributes—including neighborhood, geolocation, bed/bath count, and amenity features to recommend profit-maximizing price points that remain competitively positioned in the local market.

Seattle Airbnb Price Prediction 

Dream 
Visualizer

An experimental web application exploring the intersection of natural language processing, generative AI, and 3D visualization.

How it works:

  • A custom prompt engine analyzes dream text for sentiment and thematic keywords. 

  • Based on analysis, the engine constructs optimized prompts with style modifiers, mood descriptors, and complexity hints

  • Generated images are converted to interactive 3D point clouds using Three.js, where each pixel becomes a positioned particle colored by its original RGB value

Technical details:

  • Next.js 16 with App Router

  • TypeScript with strict mode

  • Three.js for WebGL rendering

  • IndexedDB via Dexie.js for client-side persistence

  • Property-based testing with Vitest and fast-check

  • Gemini/Nano Banana Pro for image generation

Get in Touch

For inquiries, collaborations, or to discuss your next project, feel free to reach out. I'm excited to hear from you and explore how we can bring your design visions to life.

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