I work across systems engineering, low-level software, and infrastructure-oriented development, with a background spanning electronics, firmware, distributed systems, and modern AI platforms.
I build close to the hardware — C, Rust, Assembly, Python — and focus on architecture, reliability, and performance. My perspective was shaped in constrained environments, and that still informs how I design for efficiency, observability, and failure-aware systems.
• Systems programming and performance-aware architecture
• Hardware–software integration and embedded systems
• Reliability, safety, and platform security
• Distributed and data-intensive systems
• AI and accelerator-adjacent compute
• ML infrastructure and lifecycle governance
I have worked with Azure ML, GCP ML, and cloud-native pipelines, emphasizing reproducibility, deployment, monitoring, and secure operationalization.
I am interested in building ML systems that remain:
Observable
Reproducible
Auditable
Resilient under operational and adversarial constraints
This includes data lineage, drift awareness, failure propagation, and the integration of human decision loops.
• GPU-aware pipelines and compute scheduling
• CUDA execution model literacy
• Secure model deployment and inference isolation
• Data and model versioning
• Monitoring, drift detection, and feedback loops
• Hybrid cloud and edge deployment
• Governance of large-scale AI systems
I maintain a small self-hosted lab and prefer architectures where system behavior is directly observable and controllable. Cloud platforms are tools, but I value clarity, resilience, and operational independence when required.
LinkedIn is the easiest way to reach me:
https://www.linkedin.com/in/idfoxdale