AI systems engineer / Florida Open to AI systems and evaluation roles
01

Two separate funding awards

Funded twice for Parameter Golf model training: once by OpenAI and once by Runway.

02

Former Facebook Contractor

Conversions API, Facebook Pixel, and custom JavaScript integrations.

03

Frontier compute

Provisioned multiple rented systems in every GPU class used: B200, B300, H200, and A100.

Build · operate · evaluate

AI systems.Production work.

I'm Francis Clase, an AI systems engineer who received two separate Parameter Golf training awards: one from OpenAI and one from Runway. My portfolio spans model training, agent evaluation, developer tools, location intelligence, and production operations.

Portrait of Francis Clase

Francis Clase

AI systems · data · operations

FC / 2026

8,000+

search keywords grown

14 → 16

Next.js production range

B200 → B300

frontier GPU training

6

public OpenAI challenge PRs

Portfolio / 01

Selected technical work.

Production systems, public technical artifacts, and interface case studies across AI, developer infrastructure, location intelligence, and revenue operations.

01 / Location intelligence / Field interfaceNative capture · 1814 × 900 · no upscaling
Custom location intelligence map covering the Tampa Bay region
Private build / 01

Flagship / Location intelligence

Maps built around decisions.

A shareable location-intelligence product for Mac, Windows, and mobile workflows: custom search, place discovery, address context, local categories, and an interface designed for practical field decisions.

Geospatial UXApple MapsAddress dataCross-platformLocal search

Private product build · interface case study

Fiber developer and agent interface project
Public prototype / Developer infrastructure02

Fiber turns one API contract into every interface.

A public developer-product concept that transforms OpenAPI and JSON schemas into typed SDKs, live documentation, CLIs, MCP servers, and OpenAI-native agent workflows—with reviewable diffs, governed releases, and evaluation traces.

OpenAPITyped SDKsMCP serversAgent toolsEval traces
Explore the live Fiber prototype
OpenAI Parameter Golf project artwork

Model craft / Public artifacts

OpenAI Parameter Golf

Received two separate Parameter Golf model-training awards—one from OpenAI and one from Runway—with multiple rented systems in each GPU class used: B200, B300, H200, and A100. Six public submissions span adaptive quantization, JEPA, GPTQ, FlashAttention 3, Muon, and legal compression under a strict 16 MB artifact cap.

View public pull requests

Revenue operating layer

RCG

Lead capture → qualification → scheduling → attribution → follow-up

RevOps / CRM / Customer intake

River City Glass & Auto

An end-to-end sales and revenue operations stack connecting custom intake, service logic, lead management, attribution, communications, and conversion-focused customer experiences.

Visit the live experience
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E-commerce / Paid media / Local intelligence

Pruski's storefront & growth

A production Texas meat-market storefront paired with Google Ads operations, address and neighborhood intelligence, conversion signals, automated campaign controls, and local-market prospecting.

Visit the live storefront

Agent evaluation / 02

Task design and evaluation.

A practical workflow for building expert AI tasks: grounded environments, verifiable answers, explicit scoring rubrics, realistic edge cases, and calibrated difficulty.

01

Frame the work

Turn a real operating problem into a bounded task with source systems, constraints, and a defensible target answer.

02

Build the environment

Connect CRM, account data, email, chat, maps, runbooks, dashboards, or APIs into a realistic working context.

03

Define correctness

Write objective scoring criteria, edge cases, escalation rules, and failure conditions that can be checked—not hand-waved.

04

Calibrate the agent

Run, inspect, refine, and re-test until the task is difficult for the right reasons and reliable in production.

Task authoringAgent evaluationScoring rubricsRunbooksCRM dataSupport triage

Model operations / 03

10B+

Estimated cumulative tokens used

Fluent across frontier and open-source models.

High-volume hands-on use across research, coding, agent orchestration, evaluation, automation, debugging, and production delivery—not occasional prompt testing.

01

OpenAI Codex Pro

02

ChatGPT Pro / GPT-5.6

03

Claude models

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Gemini Pro

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Open-source LLMs

Capabilities / 04

Broad range. One operating standard.

Technical depth across the entire path from acquisition and intake to automation, intelligence, and evaluation.

01

AI systems

Custom Claude agents, agent-to-agent orchestration, agent harnesses, task environments, model evaluation, scoring rubrics, and human review.

02

Revenue operations

Salesforce-certified CRM architecture, lead routing, account reconciliation, fulfillment workflows, dashboards, scheduling, and lifecycle email.

03

Data & location

Geospatial interfaces, custom maps, address intelligence, territory analysis, local search, and data-quality operations.

04

Growth engineering

Meta Conversions API, Facebook Pixel, TikTok and paid-media automation, SEO systems, reputation management, and prospecting.

05

Product engineering

Python-certified development, Next.js 14–16, TypeScript, JavaScript, APIs, e-commerce automation, and iOS/Android products.

06

Applied ML

Machine-learning training, prediction networks, Alpaca market automation, multimodal systems, and custom AI video with HyperFrames.

Contact / 05

Projects, roles, and collaboration.

AI evaluation, revenue operations, location intelligence, automation, or a product that has to work across systems—I'm interested in concrete problems with measurable outcomes.

Francis Clase

AI systems, agent evaluation, location intelligence, revenue operations, product engineering, and independent machine-learning research.

© 2026 Francis ClaseBased in Florida · Shared publicly