Edge AI Engineer Resume Example
A Edge AI Engineer profile lands when it demonstrates optimized neural nets on edge devices for inspection and on-device inference without padding. Edge AI Engineer with 4+ years deploying optimized neural networks on edge devices for industrial inspection, smart surveillance, and agricultural sensing.
Study how the example ties Python, TensorRT, ONNX, TFLite, OpenCV to delivery evidence, then rewrite with your verified timeline and stack.
Edge AI Engineer Resume Sample
Vivek Nambiar
Edge AI Engineer
Pune, Maharashtra · vivek.nambiar@email.com · +91-9823001144 · linkedin.com/in/viveknambiar-edgeai
Professional Summary
Edge AI Engineer with 4+ years deploying optimized neural networks on edge devices for industrial inspection, smart surveillance, and agricultural sensing. Expert in model quantization, TensorRT, ONNX, and deployment on NVIDIA Jetson, Coral TPU, and STM32 microcontrollers. Achieved 60x inference speed-up vs cloud-based approach with 94% model accuracy retention.
Edge AI Engineer Technical Skills
Core Skills: Python · TensorRT · ONNX · TFLite · OpenCV · NVIDIA Jetson · Coral TPU · STM32CubeAI · PyTorch · TensorFlow · INT8 Quantization · Pruning · Knowledge Distillation · ROS 2 · C++ · Docker · MQTT · AWS IoT · CMake
Professional Experience
- Deployed YOLOv8 defect detection model on NVIDIA Jetson AGX Orin for manufacturing line inspection — achieving 120fps inference vs 2fps on original server-based approach with 96.2% mAP.
- Optimized ResNet-50 backbone from FP32 to INT8 using TensorRT PTQ calibration — 60% latency reduction and 70% memory footprint reduction with <2% accuracy loss.
- Developed C++ TensorRT inference engine with pre/post-processing pipeline achieving sub-8ms end-to-end latency for 640×640 input resolution.
- Ported MobileNetV3 classifier to Coral Edge TPU using TFLite INT8 quantization — achieving 400fps inference on $60 hardware, enabling cost-effective deployment at 10,000+ sites.
- Built continuous model update pipeline: cloud retraining → ONNX export → TensorRT compilation → OTA push to 500+ edge devices with A/B rollout.
- Contributed to internal edge AI platform standardizing model packaging, versioning, and deployment manifests across 5 product programs.
- Optimized blood cell classification model for edge deployment on custom medical device — reduced model size from 45MB to 3.2MB using structured pruning and TFLite conversion.
- Wrote C++ OpenCV pre-processing pipeline for microscopy image normalization running on ARM Cortex-A72.
- Benchmarked inference performance across Jetson Nano, Jetson Xavier NX, and Raspberry Pi 4 — produced hardware selection guide used for 3 product configurations.
Edge AI Engineer Projects
CLI tool packaging PyTorch/TF models to TensorRT or TFLite with automatic calibration dataset handling and device-specific optimization presets — used by 4 teams internally.
Education
M.Tech Computer Vision — IIT Bombay, 2020 | CGPA: 8.8/10
Certifications
- NVIDIA Deep Learning Institute — Accelerating Inference with TensorRT
- AWS Certified IoT Developer
All details in this resume example are illustrative and should be replaced with your actual experience, achievements, education, and certifications.
Practical Edge AI Engineer resume guidance focused on optimized neural nets on edge devices for inspection and on-device inference, using only claims you can verify from your own history.
How to Write a Edge AI Engineer Resume
Interviewers need proof of optimized neural nets on edge devices for inspection and on-device inference, not an undifferentiated cloud of neighboring tools.
Ground depth in Python, TensorRT, ONNX, TFLite, OpenCV by linking each skill to a responsibility from your summary, experience, or edge-deploy-cli.
Resumes stumble when they cloud ML Engineer language without edge runtime constraints. Keep every technology claim tied to something you personally owned.
Prefer decision language—what you modeled, operated, secured, led, or shipped—over tool inventories that could fit any adjacent title.
Close the loop by showing how TensorRT/ONNX/TFLite deployment on Jetson/Coral-class hardware appears in your bullets, projects, and summary without inventing employers, percentages, or scale.
Experienced professional skilled in many modern tools related to edge ai engineer.
Ported MobileNetV3 classifier to Coral Edge TPU using TFLite INT8 quantization — achieving 400fps inference on $60 hardware, enabling cost-effective deployment at 10,000+ sites.
What to Include in a Edge AI Engineer Resume
Cover Python, TensorRT, ONNX, TFLite, OpenCV, NVIDIA Jetson, Coral TPU, STM32CubeAI when truthful, grouped the way you actually practiced the work rather than as a buzzword dump.
Add NVIDIA Deep Learning Institute — Accelerating Inference with TensorRT or AWS Certified IoT Developer only if completed, preserving official credential names.
Include edge-deploy-cli with technologies such as Python, TensorRT, ONNX, TFLite when you need compact proof alongside employment bullets. Add a certifications subsection because this source includes NVIDIA Deep Learning Institute — Accelerating Inference with TensorRT; AWS Certified IoT Developer; on your resume, list only credentials you actually hold and preserve their official names.
Edge AI Engineer Resume Summary Example
Begin with 4 years centered on optimized neural nets on edge devices for inspection and on-device inference, then reinforce the strongest theme already present in the professional summary.
Edge AI Engineer with 4+ years deploying optimized neural networks on edge devices for industrial inspection, smart surveillance, and agricultural sensing. Expert in model quantization, TensorRT, ONNX, and deployment on NVIDIA Jetson, Coral TPU, and STM32 microcontrollers. Achieved 60x inference speed-up vs cloud-based approach with 94% model accuracy retention.
Important Edge AI Engineer Skills for a Resume
Core Skills
Python · TensorRT · ONNX · TFLite · OpenCV · NVIDIA Jetson · Coral TPU · STM32CubeAI · PyTorch · TensorFlow · INT8 Quantization · Pruning · Knowledge Distillation · ROS 2 · C++ · Docker · MQTT · AWS IoT · CMake
Retain Edge AI Engineer skills you can defend with a delivery story, design choice, incident, test, leadership example, or project walkthrough.
Edge AI Engineer Resume Experience Examples
Senior Edge AI Engineer
Ported MobileNetV3 classifier to Coral Edge TPU using TFLite INT8 quantization — achieving 400fps inference on $60 hardware, enabling cost-effective deployment at 10,000+ sites.
Senior Edge AI Engineer
Built continuous model update pipeline: cloud retraining → ONNX export → TensorRT compilation → OTA push to 500+ edge devices with A/B rollout.
Computer Vision Engineer
Optimized blood cell classification model for edge deployment on custom medical device — reduced model size from 45MB to 3.2MB using structured pruning and TFLite conversion.
Senior Edge AI Engineer
Deployed YOLOv8 defect detection model on NVIDIA Jetson AGX Orin for manufacturing line inspection — achieving 120fps inference vs 2fps on original server-based approach with 96.2% mAP.
Use real numbers when you can verify them. Do not invent metrics simply to make the resume sound stronger.
Edge AI Engineer ATS Keywords
Choose keywords that match both the Edge AI Engineer job description and work you can substantiate. Spell out important concepts naturally in summary and experience instead of pasting this list.
Edge AI Engineer Resume Tips
Lead with on-device inference
Open with edge deployments you owned.
Show optimization stacks
Mention TensorRT, ONNX, or TFLite only when used.
Place hardware carefully
Include Jetson, Coral, or STM32 targets when accurate.
Differentiate from Computer Vision Engineer
Emphasize edge deployment constraints over model research alone.
No invented FPS gains
Skip unsupported latency/FPS claims.
Protect credibility
Drop unsupported industries, leadership claims, or tooling that never appeared in your Edge AI Engineer work.
Frequently Asked Questions
How do I prove ownership of optimized neural nets on edge devices for inspection and on-device inference on a Edge AI Engineer resume?
Cover optimized neural nets on edge devices for inspection and on-device inference with source-backed skills such as Python, TensorRT, ONNX, TFLite, OpenCV, plus experience or projects that show what you personally owned.
Which Edge AI Engineer skills belong in the skills section?
Prioritize Python, TensorRT, ONNX, TFLite, OpenCV and other category skills only when you can explain them with a project, production example, or troubleshooting story.
What should the Edge AI Engineer summary emphasize?
State about 4 years of Edge AI Engineer work and the TensorRT/ONNX/TFLite deployment on Jetson/Coral-class hardware focus that matches the job description—only if that tenure is true for you.
Can edge-deploy-cli support a thin experience section?
Yes—edge-deploy-cli can support claims involving Python, TensorRT, ONNX, TFLite when you need concise, technology-specific project evidence.
Should I list credentials such as NVIDIA Deep Learning Institute — Accelerating Inference with TensorRT on a Edge AI Engineer resume?
NVIDIA Deep Learning Institute — Accelerating Inference with TensorRT or AWS Certified IoT Developer belongs on the resume only when earned; otherwise rely on skills and delivery evidence.
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