AI / ML Engineer • Data Scientist • Research Engineer

Joshua Wolfe

Building real-time AI systems, agentic workflows, and production-ready cloud platforms.

Data scientist and AI/ML engineer with a Master of Science in Data Science and a strong cloud engineering foundation. I combine Python, Java, SQL, R, FastAPI, WebSockets, AWS, Docker, Kubernetes, and observability practices to move ideas from research into working AI-enabled systems. My current focus is LLM application engineering, agentic AI workflows, adaptive simulations, and practical ML deployment.

📍 Remote, United States 📞 470-512-7767 ✉️ hunterwolfej@icloud.com 🔗 LinkedIn 💻 GitHub
🔥 Launch Labyrinth AI Engine 📊 View ML Portfolio ⭐ GitHub Repo

LLM & Agentic AI Positioning

Agentic System Design

Designing modular AI workflows that can reason over state, coordinate tools, and support adaptive simulation behavior.

LLM Application Engineering

Building API-first AI applications with backend services, structured data flows, prompt-driven orchestration, and production deployment awareness.

Research-to-Production ML

Connecting experimentation, model evaluation, observability, and cloud infrastructure so prototypes can become usable products.

AI Platform Reliability

Applying DevOps, monitoring, containers, and cloud architecture to improve reliability, latency, and operational confidence.

AI Engineering Work

🔥 Labyrinth of Tartarus — Adaptive AI Simulation Engine

Real-time AI-powered dungeon crawler and adaptive simulation engine built with FastAPI, WebSockets, SQLite, and Python.

Live App Source Activity
PythonFastAPIWebSocketsSQLiteAgentic AIStateful SimulationRenderGitHub Actions Ready
<250ms*Target local API response latency
Real-timeBidirectional WebSocket interactions
ModularExtensible AI workflow architecture
  • Built backend services for low-latency communication, session state, and extensible AI-driven gameplay workflows.
  • Structured the project for rapid experimentation with future model integrations, tool calls, memory, and autonomous agent behaviors.
  • *Quantified metrics are labeled as engineering targets/placeholders until live benchmarks are captured from production telemetry.

📊 Applied ML & Forecasting Portfolio

Selected machine learning, predictive modeling, statistical analysis, and LSTM/time-series work.

Portfolio GitHub Repositories
PythonPandasNumPyscikit-learnTensorFlowKerasLSTMTableau/Grafana
15–30%*Target error reduction through feature/model iteration
LSTMSequence modeling for forecasting use cases
End-to-endData prep → training → evaluation → deployment planning
  • Developed ML and time-series prototypes using Python, TensorFlow/Keras, and statistical analysis techniques.
  • Performed data preparation, feature engineering, model training, and evaluation workflows to transform raw data into actionable insights.
  • *Replace target metrics with exact accuracy, MAE/RMSE, F1, or latency values once project benchmark results are finalized.

Core Competencies

🤖 AI / ML

Predictive modeling, statistical analysis, LSTM/time-series forecasting, model evaluation, prompt-driven AI workflows.

🧠 LLM / Agentic AI

Agent workflows, stateful interactions, tool-oriented architecture, AI product prototyping, research engineering.

💻 Engineering

Python, Java, SQL, R, FastAPI, WebSockets, REST APIs, SQLite, ETL/data pipelines.

☁️ Cloud / DevOps

AWS, Azure, Docker, Kubernetes, Terraform, CloudFormation, CI/CD, AWS CLI, observability.

📈 Visualization

Tableau, Grafana, dashboards, synthetic monitoring, operational analytics, stakeholder reporting.

🛡️ Infrastructure

RHEL, Active Directory, system administration, monitoring/alerting, backup, disaster recovery, security controls.

GitHub Stars & Activity

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Activity ↗Commit history

Professional Experience

Senior DevOps & Cloud Engineering Solutions Supervisor
Mar 2022 – Apr 2026
Ollion.com
  • Managed 36 Cloud Solution Engineers with direct people leadership across AWS and Azure engagements.
  • Implemented infrastructure as code with Terraform and CloudFormation to standardize environments and improve deployment consistency.
  • Established CI/CD pipelines and observability practices that improved delivery speed, system visibility, and operational reliability.
  • Mentored engineers on cloud architecture, automation, and platform best practices in complex enterprise environments.
Systems Administrator
Jun 2015 – Mar 2022
Huntington Ingalls
  • Administered enterprise systems, network security controls, backup/disaster recovery, and patching for mission-critical environments.
  • Managed Active Directory services and provided advanced troubleshooting for complex hardware and software issues.
  • Automated routine maintenance tasks with PowerShell to reduce manual effort and improve operational efficiency.
Customer Support Supervisor
Apr 2009 – Feb 2022
Verizon
  • Led a team of 15 customer service representatives and coached performance against service and quality goals.
  • Resolved escalated technical/service issues while balancing staffing, scheduling, and operational coverage.

Education

Master of Science in Data Science

University of Phoenix, 2026

Bachelor of Science in Information Technology

University of Phoenix, 2021

Honors

University of Phoenix President’s List — Jan–Jun 2025 • University of Phoenix Dean’s List — 2018

© Joshua Wolfe • AI/ML Engineering Portfolio • Built for GitHub Pages / static hosting