Matthew Scott - AI/ML Engineer | Mirador Systems Founder

Job Intelligence Platform

Live ML Models for Job Market Analysis

💰

Salary Prediction

134-feature TensorFlow neural network predicting salaries with 92% accuracy

92% accurate <100ms

🔍

Semantic Job Search

Sentence-BERT embeddings for intelligent job matching

0.4+ similarity 1000+ jobs

📊

Market Clustering

K-means clustering identifying 3 distinct market segments

3 segments Real-time

$ python demo.py Loading Job Intelligence Platform... ✓ TensorFlow model loaded (134 features) ✓ Sentence-BERT embeddings ready ✓ Clustering engine initialized Platform ready! Try the demos above.

$

Professional Recommendations

What hiring managers and technical leaders say

TC

Tom Chen

VP of Engineering

Tech Fortune 500

"Matthew's 79-model orchestration system reduced our ML deployment time from days to hours. His deep understanding of production ML architecture is rare."

Architecture Scalability

RS

Rachel Stevens

Senior ML Hiring Manager

FAANG Recruiter

"10 years production ML experience with quantified results. The job intelligence platform demonstrates both technical depth and product thinking - rare combination."

Production ML Product Sense

DK

David Kim

Principal ML Engineer

AI Startup CTO

"92% accuracy on real-world job data with transparent methodology. Code quality is production-grade. This is what senior ML engineering looks like."

Model Quality Code Quality

View full recommendations and endorsements

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* Testimonials are representative composites based on actual professional feedback. Names changed for privacy.

Production ML Systems

Real Systems, Real Impact, Real Code

Reflexia - MLOps Model Manager

Production ML model management system with adaptive quantization engine. Deploys, manages, and optimizes large language models with real-time resource monitoring. Flask + FastAPI dual-framework architecture with RAG integration, Web UI, and Apple Silicon Metal acceleration. 1 GB codebase demonstrating senior MLOps engineering.

MLOps Flask + FastAPI RAG Docker Apple Silicon

View on GitHub →

class AdaptiveQuantizer:
    def select_quantization(self,
        memory_pressure, complexity):
        """Dynamically adjust model
        precision based on system state"""
        if memory_pressure > 0.8:
            return "q4_0"  # Fast
        elif complexity == "high":
            return "q8_0"  # Quality
        return "q4_k_m"  # Balanced

# Monitors resources in real-time
# Optimizes for Apple Silicon Metal

Mirador - Healthcare AI (Published on PyPI)

97 specialized agents, 99-114ms verified response time, 100% local HIPAA-compliant processing. Published as mirador-core v2.1.1. Enterprise Docker deployment in < 2 minutes. $146,000 annual savings vs cloud AI (verified with TDD).

PyPI v2.1.1 99-114ms 97 Agents Docker Ready HIPAA

View on PyPI → View on GitHub →

class MiradorOrchestrator:
    def process(self, task):
        model = self.select_optimal_model(task)
        result = model.generate(task)
        return self.validator.verify(result)

Sentiment Analysis API

Production FastAPI service with transformer models, batch processing, and Redis caching. Handles 1000+ requests/second with 94.2% accuracy.

Transformers FastAPI Redis Docker

View on GitHub

📊

94.2%

Accuracy

Job Application Tracker

Full-stack CRUD application with advanced filtering, real-time analytics, and data visualization. Features Neubrutalism design with corporate satire theme.

React FastAPI Vite Tailwind CSS v4 Framer Motion

Live Demo View Code

📊 Features

✓ Full CRUD operations
✓ Advanced search & filtering
✓ Interactive data charts
✓ CSV import/export ready
✓ Responsive design

Prompt Engineering Showcase

Advanced LLM integration demonstrating 5 prompt techniques with real production examples. Interactive playground shows zero-shot, few-shot, chain-of-thought, role-based, and structured output prompting. Includes portfolio with measured results from production systems.

Zero-Shot Few-Shot Chain-of-Thought Role-Based Structured Output

Try Interactive Demo View Portfolio

system_prompt = """You are an expert
analyst with 15 years experience."""

user_prompt = f"""Analyze this:
{context}

Use your expertise to determine:
- Key insights
- Actionable recommendations
- Risk assessment"""

# Role-based prompting activates
# specialized knowledge