Jackson Baxter
AI Engineer & Machine Learning Specialist
Leading teams to build production RAG systems and LLM-powered applications. Specializing in semantic search, vector databases, and cloud-scale AI deployments that deliver measurable business impact.
About Me
Background
I'm a passionate AI Engineer and Machine Learning specialist with expertise in Retrieval-Augmented Generation (RAG) systems, large language models, and intelligent document processing. I graduated with a Bachelor's in Computer Science with an emphasis in Data Science and Machine Learning from Brigham Young University.
My experience spans from leading AI engineering teams at Lawrence Livermore National Laboratory to developing full-stack RAG applications and LLM-powered systems. I'm driven by the challenge of transforming complex data into actionable insights and creating AI solutions that deliver measurable business impact.
Education
BS Computer Science - Data Science
Brigham Young University
Status
Graduated April 2025
Seeking full-time AI/ML opportunities
Career Goals
I'm seeking opportunities in AI engineering, machine learning engineering, and data science where I can leverage my experience in RAG systems, LLMs, and cloud-scale AI deployments. My goal is to work on cutting-edge AI projects that solve real-world problems and drive innovation in areas like natural language processing, semantic search, and intelligent automation. I am available for remote work worldwide and have a proven track record of leading teams and delivering measurable business impact.
Featured Projects
Key Highlights:
- Reduced document search time from 2 hours to under 5 seconds
- Achieved $2.86M in estimated annual cost savings
- Improved retrieval accuracy by 35% with novel contextual embedding strategy
- Optimized for 10+ million vector corpus with sub-second retrieval
Technologies:
Key Highlights:
- Adopted by 100+ students for technical documentation queries
- Serverless AWS architecture for scalability
- Semantic search with Bedrock Titan Embeddings V2
- Improved research efficiency for academic users
Technologies:
Key Highlights:
- Deployed to support dozens of small businesses in South America
- Natural language to SQL translation using GPT-4
- Enabled non-technical business owners to access critical data
- Improved data accessibility and decision-making processes
Technologies:
Key Highlights:
- 98% precision for non-churned customer identification
- Analyzed 440,832 training records with 12 features
- Implemented both dense and residual network architectures
Technologies:
Technical Skills
Experience
Download ResumeLed a team of 3 engineers to develop a custom Retrieval-Augmented Generation (RAG) system, achieving dramatic improvements in research efficiency and cost savings.
Key Achievements:
- Led a team of 3 engineers to develop a custom RAG system, reducing document search time from 2 hours to under 5 seconds, saving an estimated $2.86M in annual costs
- Engineered a serverless data ingestion pipeline using Docling, OpenAI GPT-4o-mini, and Qwen embeddings, increasing data processing throughput by 40%
- Improved retrieval accuracy by 35% through a novel contextual embedding strategy, enhancing reliability for mission-critical research
- Built and deployed an intuitive Streamlit web interface adopted by 95% of the target research team within the first month
- Architected LanceDB vector database schema for sub-second retrieval speeds across 10+ million vectors
- Reduced requests for data retrieval assistance by 80% through improved system usability
Technologies Used:
Led IT infrastructure upgrades and managed enterprise systems for academic departments, focusing on database optimization and user management.
Key Achievements:
- Led IT infrastructure upgrades for academic departments, including database server migration and optimization, resulting in 15% improvement in query performance
- Managed Windows Active Directory for 500+ users, reducing user provisioning time by 25% through streamlined access control protocols
- Provided technical support and training for university database systems, contributing to 30% reduction in recurring support tickets
- Implemented new software and hardware solutions into university systems
- Maintained database servers and optimized system performance for academic operations
Technologies Used:
Get In Touch
Let's Connect
I'm actively seeking opportunities in AI engineering, machine learning engineering, and data science. With experience leading teams and delivering production AI systems that generate millions in cost savings, I'm excited to discuss how I can contribute to your organization's AI initiatives. Whether you have a project in mind, want to discuss potential collaborations, or just want to connect, I'd love to hear from you.