AI Systems Engineer for Physical AI, Edge Inference, and Operational AI Systems.
I build AI systems where models meet physical infrastructure: robots, Jetson-class edge devices, runtime telemetry, safety-aware observability, and operator-assist intelligence workflows.
Production AI is moving from isolated models to deployed systems that reason over physical infrastructure, edge compute, telemetry, and real-world operations.
I am currently pursuing an M.S. in Applied Artificial Intelligence at the University of San Diego, with a portfolio focused on Physical AI, edge runtime systems, operational observability, and infrastructure-aware AI deployment.
My work is converging around one systems problem:
physical infrastructure
+ edge inference
+ runtime telemetry
+ operational observability
+ retrieval-grounded intelligence
+ human-in-the-loop review
= deployable AI systems for real-world environments
The focus is not generic AI demos.
The focus is building AI systems that can be:
- benchmarked
- observed
- validated
- deployed
- audited
- operated safely under real runtime constraints
| Priority | Track | Repository | System Focus |
|---|---|---|---|
| 1 | Physical AI / Robotics Systems | physical-ai-jetson-robotics | ROS 2, Jetson, Isaac, OpenUSD, sim-to-real validation, robot telemetry |
| 2 | Physical AI Safety and Observability | physical-ai-safety-observability | safety telemetry, human/robot interaction monitoring, edge observability, operational AI safety workflows |
| 3 | Edge AI Runtime Security | jetson-edge-ai-security | Jetson runtime monitoring, edge workload observability, anomaly detection, deployment integrity |
| 4 | Edge AI Runtime Benchmarking | mnist-deep-cnn-improved-image-classification | ONNX, TensorRT, Jetson benchmarking, latency, memory pressure, sustained inference |
| 5 | Urban Edge Vision Intelligence | urban-edge-vision-analytics | edge-deployed operational event intelligence, traffic analytics, infrastructure event summarization |
| 6 | Private 5G Telemetry Infrastructure | private-5g-data-pipeline | KPI ingestion, validation, telemetry observability, feature generation, report artifacts |
| 7 | AI-RAN Operational Intelligence | ai-ran-kpi-forecasting | RAN telemetry forecasting, congestion evidence, operational reporting |
| 8 | Telecom Customer Experience Intelligence | telecom-churn-ml-with-agents | customer-risk trajectories, intervention recommendations, network-to-customer correlation |
| 9 | Wireless Link Intelligence | qpsk-wireless-link-simulator | QPSK simulation, BER/SNR analysis, AI-assisted link estimation foundations |
| 10 | Explainable Human-Reviewed AI | agentic-medical-ai-explainability | reproducible ML, explainability, safety boundaries, human-in-the-loop reporting |
Repository: physical-ai-jetson-robotics
A Physical AI engineering platform connecting:
- ROS 2 robotics workflows
- Jetson edge inference
- OpenUSD / Isaac simulation
- robot telemetry and diagnostics
- sim-to-real validation
- safety-aware operations tooling
- retrieval-grounded diagnostics over logs, documentation, and runtime state
- AI-RAN / private 5G readiness concepts for robotics workloads
This repository is the center of gravity for the portfolio.
Physical infrastructure
-> edge inference
-> runtime telemetry
-> operational observability
-> retrieval-grounded intelligence
-> operator-assist workflows
-> telecom / wireless infrastructure support
The repositories are intentionally connected.
The broader thesis is that AI systems become operationally valuable only when connected to:
- telemetry
- runtime constraints
- evidence
- observability
- human review
- deployment workflows
| Priority | Repository | Upgrade Focus |
|---|---|---|
| 1 | physical-ai-jetson-robotics | runtime evidence, telemetry artifacts, screenshots, validation reports, sim-to-real workflows |
| 2 | physical-ai-safety-observability | operational safety telemetry, evidence chains, runtime observability, human-review workflows |
| 3 | jetson-edge-ai-security | edge runtime anomaly detection, deployment integrity, telemetry observability |
| 4 | mnist-deep-cnn-improved-image-classification | convert into a serious Edge AI Runtime Benchmarking Lab |
| 5 | telecom-churn-ml-with-agents | telecom operational intelligence and intervention workflows |
This profile repository also includes agent operating standards for AI-assisted development:
AGENTS.md— repository-level operating contract for Claude Code, Codex, Cursor, Aider, and similar coding agentsagent-skills/— review skills for architecture, runtime stability, observability, edge deployment, AI-RAN workflows, RAG/telemetry copilots, and sim-to-real validation
The goal is to keep AI-assisted development disciplined:
- small patches
- explicit scope
- measurable validation
- operational realism
- evidence-backed claims
- strict public/private boundaries
Edge AI: NVIDIA Jetson, TensorRT, vLLM, ONNX, CUDA, VLM/LLM deployment
Robotics / Physical AI: ROS 2, MoveIt 2, Isaac Sim, Isaac Lab, OpenUSD
AI / ML: Python, PyTorch, scikit-learn, XGBoost, SHAP, MLflow
Operational AI / RAG: retrieval-grounded copilots, local inference workflows, guardrails, human review
Data / Infrastructure: SQL, Spark, Airflow, dbt, Docker, Kubernetes, CI/CD
Telecom / AI-RAN: RAN telemetry, KPI forecasting, private 5G, wireless link analysis
Cloud / Distributed Systems: AWS, Azure, GCP, Terraform
- Email: obiedeh@gmail.com
- LinkedIn: linkedin.com/in/obinna-edeh-206306137
- GitHub: github.com/obiedeh