Matthew Bouyea

Core Focus

• Python backend engineering, data engineering, and LLM/analytics infrastructure
• Turning loosely defined analytic workflows into repeatable, maintainable systems
• Efficient system design where bad choices make the problem computationally intractable

Languages & Frameworks

• Python (primarily async)
• SQL (comfortable learning what I need on the fly)
• FastAPI, Flask
• Dash (for internal UIs when needed)

Data, Search & Messaging

• Elasticsearch, Logstash, Kibana (ELK stack)
• MongoDB
• RabbitMQ, Redis (caching and messaging)
• Kafka

Infrastructure & Ops

• Docker, Docker Swarm
• Kubernetes (including multi-site clusters)
• Linux
• Git, GitLab (including CI/CD basics)
• Comfortable working directly in large shared compute and cluster environments

LLM & Analytics Tooling

• Integrating local LLMs into backend services (e.g., classification APIs)
• Building OpenAI-style interfaces around internal models
• Using LLMs and diarization/transcription to automate report and minutes generation
• Using ELK as an analytics stack (beyond pure logging) for pattern discovery and dashboarding

Work Style

• Autonomous individual contributor; people bring me problems where design choices determine whether the solution is feasible on real hardware
• Systems thinker who iterates on project structure and code composition until it's predictable and reusable
• Comfortable in environments with minimal formal requirements where the job is "figure out how to make this possible"

Altamira Technologies (via acquisition of VaST)

Mission Integrator / Software Developer

2021 – Present • Augusta, GA / Remote

• Work in a small team of former analysts who upskilled into engineering to answer questions other analysts couldn't practically solve with off-the-shelf tools or by hand.
• Partner with analysts and collection operators to understand how they explore massive datasets and then turn those ad-hoc workflows into analytics frameworks and tools.
• Design and implement async Python services that sit between high-end infrastructure (Kubernetes, internal compute clusters, ELK) and non-technical analysts.
• Build end-to-end flows where analysts configure inputs via simple UIs, backend services query and join across data stores, run analysis, and then either generate structured reports at scale (thousands to millions of records) or update database records to make future exploration easier.
• Maintain and evolve more than 40 internal applications and roughly 70–80 microservices over time; a core subset of these continue to run because the designs are stable and the code "just works."
• Take responsibility for efficient system design when the wrong choice would mean saturating hardware or making the problem effectively unsolvable at required scale.

Mission Essential Personnel

Geolocation Operator & Analyst

2019 – 2021 • Augusta, GA

• Served as a geolocation operator and analyst, focused on finding, tracking, and recognizing patterns in large data streams and disseminating findings to downstream users.
• Used Elasticsearch and Kibana to explore, visualize, and understand high-volume data, often stretching the tools beyond their original use cases to answer operational questions.
• Began teaching myself to code by building small tools to speed up my own analytic workflows, laying the foundation for a full transition into engineering.

U.S. Army

Signals Intelligence Analyst (35N)

2015 – 2019 • Various Locations

• Worked as a signals intelligence analyst, geolocation operator, and mission manager in environments where accuracy, timeliness, and disciplined processes directly mattered.
• Handled large, complex datasets under pressure, coordinating analysis and reporting with operational teams.
• Developed habits that carry directly into engineering work: procedural discipline, careful handling of data, methodical problem-solving, and the ability to stay calm while things are breaking.