Staff Edge Engineer @ BrightAI
Former Principal Engineer @ Digi International
Edge AI, TinyML, Physical AI, Embedded Systems, and Wireless IoT Platforms

I specialize in embedded systems, Edge AI, and intelligent IoT infrastructure.
My work focuses on bridging low-level hardware with scalable, production-ready AIoT platforms, bringing intelligence closer to where data is generated and acted upon.

• Staff Edge Engineer at BrightAI, building Physical AI systems for real-world infrastructure and field operations.
• Building TinyMLDelta — an incremental model update system for TinyML and resource-constrained edge devices
  https://github.com/felixgalindo/TinyMLDelta
• Building toyvision-ai — an open-source project introducing AI concepts through hands-on, playful experiments
  https://github.com/felixgalindo/toyvision-ai
• Architecting a modern C library for XBee, improving developer experience and extensibility for wireless IoT platforms
  https://github.com/felixgalindo/xbee_c_library

• Principal Engineer @ Digi International
  Led and owned production systems across embedded devices, RF platforms, LoRaWAN connectivity, edge intelligence, and device-to-cloud infrastructure.
  Worked across hardware, firmware, provisioning workflows, tooling, and platform architecture for large-scale industrial and OEM IoT deployments.

  See more of my previous experiences here: 🔗 https://felixgalindo.io/#resume

• Edge Intelligence: Enabling IoT at the Source — The AI Innovator (2025)
  https://theaiinnovator.com/edge-intelligence-enabling-iot-at-the-source/

• Rethinking IoT Innovation: From Industrial Legacy Systems to Next-Gen Wi-Fi — Embedded Computing (2026)
  Editorial coverage based on the Embedded Insiders podcast discussing Industrial IoT modernization and next-generation wireless architectures.
  https://embeddedcomputing.com/technology/iot/wireless-sensor-networks/rethinking-iot-innovation-from-industrial-legacy-systems-to-next-gen-wi-fi

• Featured Guest — Embedded Insiders Podcast (January 2026)
  Discussion on the industrial upgrade crisis, legacy connectivity challenges, and the evolution toward modern edge-to-cloud platforms.
  https://www.youtube.com/watch?v=ambwtKT63s0

• Expert Panelist — Embedded World North America 2025
  Panel on practical edge intelligence deployment, production readiness, and edge-to-cloud architectures.
  YouTube: https://youtu.be/41o0cOKYkuM
  Digi Resource Page: https://www.digi.com/resources/videos/ai-at-the-edge-ewna-2025

• Featured in Digi International press announcement
  New open IoT Application Framework accelerates development by addressing the complexity of connected product deployment at scale from edge to cloud
  https://www.digi.com/company/press-releases/2025/digi-showcases-complete-edge-to-cloud-solution

• Selected Medium Articles

  • Why Edge AI Fails — And Why the Missing Infrastructure Layer Matters More Than the Models
    https://medium.com/@felixgalindo91/why-edge-ai-fails-and-why-the-missing-infrastructure-layer-matters-more-than-the-models-a05f291a5834
  • Introducing TinyMLDelta — Incremental ML Model Updates for Tiny Devices
    https://medium.com/@felixgalindo91/introducing-tinymldelta-incremental-ml-model-updates-for-tiny-devices-96663edd1991
  • Vision-Language-Action Models and What They Unlock for Edge Intelligence
    https://medium.com/@felixgalindo91/vision-language-action-models-and-what-they-unlock-for-edge-ai-65ead9f980a6

• Press

  • TinyMLDelta featured on Hackster.io
    https://www.hackster.io/news/tinymldelta-brings-safe-lightweight-updates-to-edge-ai-6ec411f93f44

More media/coverage found here:

🔗 https://felixgalindo.io/#coverage

I’ve led and owned multiple production-grade edge and IoT systems spanning embedded devices, gateways, and device-to-cloud platforms.

Detailed system architecture, technical scope, and ownership are documented here:

🔗 https://felixgalindo.io/#works

These projects focus on real-world constraints such as power, RF reliability, provisioning, security, scalability, and long-term maintainability.

• Multimodal Transformer Fusion for Vehicle Detection
  Designed and implemented a DETR-inspired transformer architecture for multimodal perception, fusing LiDAR and camera features for vehicle detection.
  Explored early-fusion and feature-level fusion strategies, representation alignment, and cross-modal attention tradeoffs relevant to real-world Physical AI perception pipelines.
  Developed as part of graduate-level Deep Learning coursework (Georgia Tech OMSCS), with emphasis on architectural reasoning and system design over benchmark optimization.
  https://github.com/felixgalindo/MultiModal-Fusion-Vehicle-Detector

• Edge AI, TinyML
• Ultra-low-power embedded systems
• Distributed decision-making on constrained devices
• Edge-to-cloud IoT platforms and production readiness
• Industrial IoT, smart infrastructure, and Physical AI systems

Email: felix@felixgalindo.io
LinkedIn: https://www.linkedin.com/in/felix-galindo/
Medium: https://medium.com/@felixgalindo91
GitHub: https://github.com/felixgalindo
Website / CV: https://felixgalindo.io/