You will get a detailed Edge AI architecture review for your Android or embedded system
Rising Talent
Project details
I will review the complete engineering path around your Edge AI system, not just the model file. The review can cover Android, embedded Linux, or MCU deployments, including model preprocessing and quantization, runtime memory and latency, data and protocol flow, JNI or native boundaries, error handling, watchdogs, and rollback behavior.
My experience includes TensorFlow Lite and TFLite Micro camera inference, PyTorch-to-ONNX time-series pipelines, Android/Kotlin/JNI integration, and industrial IoT control. I focus on difficult boundary problems that often appear during deployment: mismatched tensor contracts, unsafe concurrency, unstable communications, hidden resource limits, and incomplete failure recovery.
You will receive a written report with prioritized findings, architecture or data-flow diagrams, and practical next actions. Redacted source snippets, diagrams, logs, and model metadata are welcome. This service is a technical review. Implementation and source-code delivery are not included unless arranged under a separate contract.
My experience includes TensorFlow Lite and TFLite Micro camera inference, PyTorch-to-ONNX time-series pipelines, Android/Kotlin/JNI integration, and industrial IoT control. I focus on difficult boundary problems that often appear during deployment: mismatched tensor contracts, unsafe concurrency, unstable communications, hidden resource limits, and incomplete failure recovery.
You will receive a written report with prioritized findings, architecture or data-flow diagrams, and practical next actions. Redacted source snippets, diagrams, logs, and model metadata are welcome. This service is a technical review. Implementation and source-code delivery are not included unless arranged under a separate contract.
Machine Learning Tools
Keras, NumPy, OpenCV, Python, PyTorch, TensorFlowWhat's included
| Service Tiers |
Starter
$39
|
Standard
$89
|
Advanced
$179
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 1 | 2 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 3 | 5 |
Number of Graphs/Charts | 1 | 2 | 3 |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code | - | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$15 - $50
Additional Revision
+$15
Additional Model Variation
(+ 1 Day)
+$25
Additional Scenario
(+ 1 Day)
+$15
Additional Graph/Chart
(+ 1 Day)
+$10
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FC
Fatih C.
Jul 16, 2026
MIMXRT1062 Bare MCU (Teensyduino)
About Lee
Embedded AI Engineer | TFLite Micro - STM32 - FreeRTOS - On-Device ML
Daegu, South Korea - 6:16 am local time
Most firmware engineers can write clean embedded C. Few can take a trained neural network, compress it 4x through INT8 quantization, and deploy it on a Cortex-M4 with 50ms latency and 12mW power draw. That's what I do.
I've shipped MCU-based systems across industrial, automotive, and IoT domains for 30 years — from bare-metal bring-up to FreeRTOS task architecture. Recently I've been closing the gap between ML research and production hardware: PyTorch → ONNX → TFLite Micro, with CMSIS-NN acceleration and static memory allocation for deterministic real-time behavior.
What I deliver:
- On-device AI/ML inference on STM32, ARM Cortex-M (TFLite Micro, CMSIS-NN)
- FreeRTOS firmware with sensor fusion, SPI/I2C/UART, low-power design
- Full pipeline from model training to flashed firmware
- Production-ready code from prototype to mass production
If you're building something that needs to be smart, small, and always-on — let's talk.
Steps for completing your project
After purchasing the project, send requirements so Lee can start the project.
Delivery time starts when Lee receives requirements from you.
Lee works on your project following the steps below.
Revisions may occur after the delivery date.
Scope and Material Review
I review the submitted materials, confirm the models, scenarios, constraints, and review boundaries, and identify any missing information before analysis begins.
Architecture and Runtime Analysis
I trace the model contract, preprocessing, runtime resources, data flow, concurrency, connectivity, and failure paths against the stated requirements.