Full Stack Python, Cloud & AI Engineer

Rion Hutsell

Full Stack Python / Cloud Engineer / AI Engineer / Quality Assurance

Phone: 303-***-**** - Email: ************@*****.*** - sciencefixion.github.io Professional Summary

Full Stack Python & Cloud Engineer with 9+ years of experience spanning software development, Gen AI integration, QA, and customer-focused engineering. Specializes in designing and deploying cloud-native applications using Python, FastAPI, Flask, and AWS (EC2, S3, RDS), with deep expertise in agentic AI systems via LangChain, LangGraph, and VectorDBs. Experienced in Agile/Scrum environments with a track record of taking solutions from concept to production. Skills

Backend: Python (FastAPI, Pydantic, Flask,

SQLAlchemy, boto3), Ruby (Rails, Active Record),

Javascript (Node)Databases: VectorDB, ChromaDB,

SQL, PostgreSQL, MySQL, SQLite

Frontend: Javascript, React (TypeScript), Bootstrap, HTML / CSS

Cloud: AWS (EC2, S3, RDS, IAM, AWS Bedrock) Gen AI: LangChain, LangGraph, agents, RAG, NER, Ollama LLM models

CI/CD & Testing: GitHub Actions, AWS, Docker,

Pytest, RSpec, Selenium, Postman, Katalon, Gherkin Experience

Full Stack Gen AI Developer

January 2026 to Present

Revature

Developed AI-powered full-stack applications using Python (FastAPI), React (TypeScript), LangChain, and LangGraph, integrating local LLM models (Ollama/Mistral) with RESTful APIs and cloud-backed storage (AWS S3, SQLite, VectorDB) for intelligent data retrieval and chat workflows

Architected Retrieval-Augmented Generation (RAG) systems in Python with ChromaDB vector stores, semantic similarity search, and multi-turn conversational memory, deployed in containerized environments using Docker for scalable and portable AI solutions

Implemented Named Entity Recognition (NER) pipelines using Python and BERT-based NLP models to extract and aggregate entities, enabling intelligent query routing and cloud-integrated processing workflows on AWS

Built and consumed RESTful APIs using Python, incorporating robust input handling, batch processing, logging, and Pydantic validation, with services deployed via Docker containers and orchestrated using Kubernetes for high availability

Applied modern backend and cloud engineering practices, including modular architecture, async programming, CI/CD exposure, and deployment of microservices on AWS (EC2/Lambda) with container orchestration using Kubernetes

Python Developer / Senior Technician

September 2024 to January 2026

Allegiance Technologies & Consulting

Designed and deployed a cloud-native web application using Python (Flask) and AWS, implementing user authentication and task management features. Integrated AWS services including RDS, S3, and EC2, and automated CI/CD using AWS CodePipeline and GitHub Actions to streamline deployments

Developed scalable RESTful APIs using Python, integrating seamlessly with AWS-hosted databases and third-party vendor APIs to enhance data exchange, performance, and user functionality

Leveraged Python with SQLAlchemy ORM and Flask MVC architecture to optimize database interactions, improve code maintainability, and accelerate feature delivery in Agile development cycles

Applied Python development best practices including modular design, reusable components, debugging, and performance optimization while working in Agile/Scrum environments with sprint planning, demos, and retrospectives

Delivered customized Python-based software solutions alongside hardware support for business clients across Mac, Windows, and Linux systems, ensuring cross-platform compatibility and reliability Software Engineer

October 2022 to September 2024

Havel Development

Modernized legacy systems by introducing Python scripting and automation, supporting migration to containerized environments

Contributed to backend improvements by developing Python-based utilities for data processing and system integration

Collaborated on system design, incorporating Python components and microservices concepts aligned with business goals

Supported full project lifecycle using Python for debugging, integration, and workflow automation

Applied core Python development principles and version control (Git) in a collaborative Agile environment QA Engineer

June 2021 to September 2022

Bayard Advertising

• Established QA processes from the ground up for an automated hiring platform, introducing structured workflows and improving release quality across web and backend systems

• Designed and executed comprehensive test strategies, test plans, and test cases, aligning with Agile delivery cycles and ensuring end-to-end system validation

• Developed and maintained API and automation test scripts, leveraging Gherkin (BDD) with tools like Postman, Katalon Studio, and Zephyr Scale to enable scalable and readable test automation

• Performed backend and API validation, working closely with developers to test RESTful services, data flows, and integration points across systems

• Contributed to test automation and scripting efforts, supporting reusable testing components and improving regression testing efficiency

Genius (Apple Certified Mac Technician)

September 2015 to June 2019

Apple

Provided hands-on technical support for macOS, iOS, watchOS, and tvOS devices, diagnosing and resolving hardware and software issues through systematic troubleshooting methodologies, achieving a 93%+ customer satisfaction score

Delivered one-to-one customer training at the Genius Bar, educating users on Apple hardware and software to build product confidence, drive customer delight, and foster long-term brand loyalty

Delivered customer-focused support across the full service lifecycle — from intake to resolution — leveraging strong communication and soft skills to drive customer satisfaction, retention, and brand loyalty in high-volume, fast-paced environments

Received the Credo Award for exemplifying the embodiment of Apple's mission Education

Back End Engineering Program

Turing School of Software & Design - CO

Apple Certified Mac Technician (ACMT)

Apple Inc. - CA

B.A. : Mass Communication - Journalism

Anderson University - IN

Additional Information

FEATURED PROJECTS

• Walt-Bot: An AI-powered FastAPI app featuring agentic RAG with LangGraph and a VectorDB

• Simple Cadaver: A cloud-native exquisite corpse game configured for AWS in Python

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