Kylan Thomson AI Solutions Architect · Google Cloud

Where I've worked

Professional Experience

1. Google Cloud Solutions Engineer · GenAI · Promevo

   Sep 2025 – Present

   Designing and delivering Generative AI solutions on Google Cloud for Professional Services clients — from scoping through production.

   • Engineer custom Generative AI agents, including an agent that automates project resource allocation by ingesting project context, matching skill alignment, checking calendar availability, and creating meeting invites.
   • Developed a custom GenAI application for brand-style image creation; external demos of the app won follow-on projects and client workshops.
   • Templatized and standardized Gemini Enterprise workshop delivery, improving scalability and repeatability across the Professional Services team.
   • Re-scoped and stabilized a critical client engagement, turning a misaligned project into follow-on work discussions.
   • Scoped a complex content-platform migration for a healthcare client, including a successful proof-of-concept reconstructing proprietary pages into embeddable Google Docs.
   • Established a NotebookLM-based process for hosting client engagements, now incorporated into client demonstrations.
   • Collaborated with gPanel developers to establish best practices for the Gemini CLI, contributing to internal product and developer enablement.

   • Vertex AI
   • Gemini Enterprise
   • Agent Development Kit (ADK)
   • MCP
   • Dialogflow
   • Python
   • Google Cloud

2. Experimentation Specialist · Alaska Airlines

   Oct 2023 – Sep 2024

   AI-assisted experimentation and large-scale data engineering for a major U.S. airline.

   • Orchestrated development of LLM-powered agentic workflows that automated experiment analysis with human-in-the-loop validation.
   • Engineered Python/PySpark pipelines on Databricks for analyzing experimentation techniques, enabling data-driven product decisions.
   • Deployed a Spark-based QA pipeline processing over 100,000 transactions daily for anomaly detection and system health.
   • Implemented experiment tagging and tracking systems, surfacing actionable insights on customer behavior and revenue impact.
   • Designed and executed experiments promoting eco-friendly initiatives, driving user engagement and conversion.

   • Python
   • PySpark
   • Databricks
   • LLM Workflows
   • Spark

3. Software Engineer · Credera

   Nov 2021 – May 2023

   Cloud data engineering and full-stack delivery for enterprise consulting clients.

   • Developed real-time data streams using AWS MSK (Kafka) and Braze, enabling personalized customer engagement.
   • Implemented infrastructure as code with Terraform and configured GitHub Actions CI/CD pipelines.
   • Built Java integration test suites to ensure pipeline integrity and scalability.
   • Led website localization efforts using React and Node.js, increasing global user engagement.

   • AWS MSK / Kafka
   • Terraform
   • Java
   • GitHub Actions
   • React
   • Node.js

4. Software Engineer · UNO

   Oct 2020 – Nov 2021

   Early-stage startup engineering across web, mobile, and cloud.

   • Contributed to scalable software applications using AWS, React, Node.js, Python, and Flutter.
   • Translated business needs into technical documentation, aligning development with stakeholder goals.
   • Optimized application performance and backend operations on AWS.
   • Developed cross-platform mobile applications with Flutter.

   • AWS
   • React
   • Node.js
   • Python
   • Flutter

What I've built

AI & Cloud Projects

SporeCast

Geospatial machine learning that predicts where edible mushrooms are fruiting across Washington State — species distribution modeling over satellite embeddings, terrain, and weather-aware phenology.

20 species modeled from 13 public data sources, served as an interactive forecast map.

• Built a per-species ensemble distribution model under spatially blocked cross-validation, calibrated so scores stay comparable across species.
• Engineered 40+ covariates spanning 64-dimensional satellite embeddings, terrain, hydrology, and lapse-rate-downscaled climate normals.
• Corrected presence-only observer bias so the model learns habitat quality rather than where people happen to hike.
• Shipped the whole pipeline on keyless public APIs and server-side reductions — no raster downloads, fully reproducible.

• Python
• scikit-learn
• Google Earth Engine
• LightGBM
• Leaflet
• Cloud Run

Read the technical case study→

Resume Revival

AI-powered web application that optimizes resumes and cover letters for ATS systems, with multi-job tailoring and keyword optimization.

6,000+ job seekers served, improving interview chances through keyword-optimized resumes.

• Architected and shipped a production GenAI application on Google Cloud Functions with Gemini integration.
• Engineered an AI pipeline that generates keyword-rich, ATS-friendly resumes and cover letters tailored to specific job descriptions.
• Built multi-job tailoring and skill-gap analysis features that show candidates exactly what to develop for their target role.

• Google Cloud Functions
• Gemini
• React
• Python
• API Development

Wildfire Sentinel

A live-data wildfire simulator and response platform: pick any place on Earth and any historical date range, watch a physically-grounded fire fight real terrain, buildings, and weather — then race autonomous drone doctrines to find what would have saved the most.

Reproduces the 2025 Palisades fire to within order-of-magnitude on structures lost with no per-fire tuning — from 4 live public data sources and zero API keys.

• Rebuilt a hackathon prototype into a self-contained simulator that assembles any scenario on demand from four key-less public APIs — 30 m elevation, 45,930 OpenStreetMap structures, water polygons, and hourly ERA5 weather — with no backend and a fallback for every source.
• Replaced probabilistic cell flips with a physical spread engine: rate of spread in metres per hour shaped by an elliptical wind field, lagged fuel moisture, slope, and ember spotting, which is what lets the model be validated against a real fire rather than tuned to one.
• Built a Strategy Lab that clones a paused incident and races response doctrines against seeded-identical fires, so outcome differences are attributable to strategy rather than luck, and the winning algorithm can take over the live simulation in one click.
• Added a Monte Carlo damage forecaster projecting burn probability and value-weighted expected loss 24–72 hours ahead, chunked through the event loop to keep the interface responsive.

• React
• JavaScript
• Canvas
• Three.js
• Monte Carlo
• Geospatial APIs

Read the technical case study→

AI Stock Image Factory

A demand-driven generative pipeline that reads search-volume data, generates stock imagery aimed at what buyers actually search for, and engineers the metadata that makes it findable.

5,110 downloads and $3,840 in royalties earned on Adobe Stock, from a fully automated generation pipeline.

• Built an end-to-end pipeline that samples target keywords weighted by monthly search volume, so production tracks real buyer demand rather than working a list top-down.
• Engineered the metadata path that actually drives sales: model-generated keywords are deduplicated, re-ranked by embedding similarity to the target term, and filtered, so the highest-intent terms land in the slots the marketplace weights most.
• Layered a library of 72 named house styles over fixed core instructions, keeping output visually varied but consistent in quality — and model-agnostic enough to swap image models as a config change.
• Made the model stack fully config-driven with per-stage tiering, holding cost to a few cents per asset while reserving spend for image generation.

• Python
• Generative AI
• Prompt Engineering
• Embeddings
• Content Ops
• Cloud Run

Read the technical case study→

What I work with

Skills & Technologies

Credentials

Certifications & Education

Get in touch

Let's Build Something on Google Cloud