Career Story
Scientific computing, enterprise software, security and platform engineering, production ownership, and applied AI are cumulative parts of one engineering path. Historical role titles remain exactly what they were.
Each chapter adds a system boundary, operating constraint, or evidence discipline that carries into the work today.
Chapter 1
Evidence: The Augsburg space-physics record includes magnetometer-data analysis for EMIC-wave and magnetospheric research, two 2017 AGU abstracts, and a collaborative 2018 JGR: Space Physics paper. That study incorporated radiation-belt measurements from REPT and MagEIS aboard NASA's Van Allen Probes.
What it adds: Evidence quality, reproducibility, and careful interpretation became part of the engineering approach from the beginning.
Chapter 2
Evidence: Application and platform work expanded across e-commerce, React, Kotlin microservices, Kubernetes delivery, and modernization in large operating environments.
What it adds: The work grew from features into the interfaces, services, repositories, and delivery paths that make software operable at enterprise scale.
Chapter 3
Evidence: Work at GE Aerospace and Securian covered application security, API hardening, OpenShift, Ansible, AWS infrastructure, Kubernetes support, and workflow automation.
What it adds: Security, infrastructure, automation, and reliability became design inputs rather than post-build concerns.
Chapter 4
Evidence: Founder and Principal Software Engineer work spans architecture, application development, infrastructure, deployment, security, and support. VIFG is the clearest public proof, live since 2020.
What it adds: Architecture decisions are tested against deployment, accessibility, maintenance, and the people responsible after launch.
Chapter 5
Evidence: Graduate AI and Big Data study, accepted retrieval research, my lead implementation work on the collaborative WeatherForge academic project, and the bounded RAGeATM prototype extend the foundation into data engineering, grounding, evaluation, and refusal behavior.
What it adds: AI is treated as one capability inside a larger production system, with evidence and limits kept explicit.
Current synthesis
AI Systems & Platform Engineer
AI Systems & Platform Engineer is David Braun's canonical professional positioning, not a retroactive employment title. Individual roles, projects, and research remain labeled by their recorded title and evidence maturity.
The career path expanded from individual features and analyses into the connected layers required to operate a dependable system.
Grounded AI workflows, retrieval, refusal behavior, and quality evaluation.
User-facing applications, service boundaries, queues, routing, and human review.
Cloud services, containers, delivery pipelines, networking, and runtime environments.
Schemas, databases, pipelines, state, jobs, and event-driven coordination.
Identity, access, validation, testing, observability, and failure handling.
Deployment, accessibility, documentation, handoff, maintenance, and stewardship.
The same cross-layer background supports embedded engineering roles and bounded consulting engagements.
Production systems, pilots, prototypes, research, and historical roles are deliberately labeled according to what their supporting evidence establishes.
How I label and verify technical work