OVA.Work
Posted 67 days agoVerified live 5h ago
Edge AI Engineer
Brief overview
Remote
3+ yrsMinimum
Machine LearningDeep LearningNeural NetworksComputer VisionNatural Language ProcessingModel QuantizationModel PruningKnowledge DistillationTensorFlow LiteTensorFlowPyTorchONNX RuntimeTensorRTOpenVINOQualcomm AI Engine SDKNVIDIA JetPackMediaPipe
About the company
OVA.Workova.work
OVA is the most advanced Automated, Intelligent, intuitive On-boarding platform for Staffing Firms of all sizes.
Job description
Summary
OVA.Work is seeking an experienced Edge AI Engineer to design, develop, optimize, and deploy AI and Machine Learning models on edge devices. The role involves working with embedded systems and ensuring the deployment of AI solutions to various devices while maintaining high performance and energy efficiency.
Responsibilities
- Design, develop, and deploy AI/ML models on edge devices
- Optimize deep learning models for resource-constrained hardware
- Develop computer vision and real-time inference applications
- Convert and optimize models using TensorFlow Lite, ONNX, TensorRT, or OpenVINO
- Integrate AI models with embedded firmware and IoT platforms
- Work with hardware accelerators such as GPUs, TPUs, and NPUs
- Optimize latency, memory usage, and power consumption
- Develop edge inference pipelines for real-time applications
- Collaborate with AI researchers, embedded engineers, cloud engineers, and product teams
- Validate AI model accuracy, robustness, and performance
- Maintain technical documentation and deployment pipelines
Skills
- Machine Learning
- Deep Learning
- Neural Networks
- Computer Vision
- Natural Language Processing (NLP)
- Reinforcement Learning (Preferred)
- Transfer Learning
- Model Quantization
- Model Pruning
- Knowledge Distillation
- TensorFlow Lite
- TensorFlow
- PyTorch
- ONNX Runtime
- TensorRT
- OpenVINO
- Qualcomm AI Engine SDK
- NVIDIA JetPack
- MediaPipe
- Python
- C++
- C
- Java (Preferred)
- Bash
- Embedded Linux
- ARM Cortex
- Raspberry Pi
- NVIDIA Jetson
- Google Coral
- TPU
- ESP32
- STM32
- NXP Platforms
- Qualcomm Snapdragon
- OpenCV
- YOLO
- SSD
- Faster R-CNN
- Image Classification
- Object Detection
- Object Tracking
- Image Segmentation
- OCR
- Quantization
- Pruning
- Mixed Precision
- Tensor Optimization
- Edge Inference
- Hardware Acceleration
- Edge Computing
- IoT Architecture
- MQTT
- OPC UA
- Edge Gateway
- Device Management
- OTA Updates
- AWS IoT
- Azure IoT Hub
- Google Cloud IoT
- Azure Machine Learning
- AWS SageMaker
- Vertex AI
- GPU
- TPU
- NPU
- CUDA
- CuDNN
- Client
- Movidius
- Edge TPU
- Git
- GitHub
- GitLab
- Jenkins
- Docker
- Kubernetes (Basic)
- CI/CD
- Linux
- Ubuntu
- Embedded Linux
- RTOS (Preferred)
- Strong analytical and problem-solving skills
- Excellent communication and teamwork
- Ability to optimize complex AI workloads
- Attention to detail
- Continuous learning mindset
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Electronics, Embedded Systems, Robotics, or a related field
- 3–8+ years of experience in AI/ML and embedded systems
- Hands-on experience deploying AI models to edge devices
- Strong understanding of computer vision and deep learning
- TensorFlow Developer Certificate
- NVIDIA Deep Learning Institute (DLI)
- Microsoft Azure AI Engineer Associate (AI-102)
- Google Professional Machine Learning Engineer
- AWS Certified Machine Learning – Specialty
Qualifications
Must Haves
- Machine Learning
- Deep Learning
- Neural Networks
- Computer Vision
- Natural Language Processing (NLP)
- Reinforcement Learning (Preferred)
- Transfer Learning
- Model Quantization
- Model Pruning
- Knowledge Distillation
- TensorFlow Lite
- TensorFlow
- PyTorch
- ONNX Runtime
- TensorRT
- OpenVINO
- Qualcomm AI Engine SDK
- NVIDIA JetPack
- MediaPipe
- Python
- C++
- C
- Java (Preferred)
- Bash
- Embedded Linux
- ARM Cortex
- Raspberry Pi
- NVIDIA Jetson
- Google Coral
- TPU
- ESP32
- STM32
- NXP Platforms
- Qualcomm Snapdragon
- OpenCV
- YOLO
- SSD
- Faster R-CNN
- Image Classification
- Object Detection
- Object Tracking
- Image Segmentation
- OCR
- Quantization
- Pruning
- Mixed Precision
- Tensor Optimization
- Edge Inference
- Hardware Acceleration
- Edge Computing
- IoT Architecture
- MQTT
- OPC UA
- Edge Gateway
- Device Management
- OTA Updates
- AWS IoT
- Azure IoT Hub
- Google Cloud IoT
- Azure Machine Learning
- AWS SageMaker
- Vertex AI
- GPU
- TPU
- NPU
- CUDA
- cuDNN
- Client
- Movidius
- Edge TPU
- Git
- GitHub
- GitLab
- Jenkins
- Docker
- Kubernetes (Basic)
- CI/CD
- Linux
- Ubuntu
- Embedded Linux
- RTOS (Preferred)
- Strong analytical and problem-solving skills
- Excellent communication and teamwork
- Ability to optimize complex AI workloads
- Attention to detail
- Continuous learning mindset
Nice to Haves
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Electronics, Embedded Systems, Robotics, or a related field
- 3–8+ years of experience in AI/ML and embedded systems
- Hands-on experience deploying AI models to edge devices
- Strong understanding of computer vision and deep learning
- TensorFlow Developer Certificate
- NVIDIA Deep Learning Institute (DLI)
- Microsoft Azure AI Engineer Associate (AI-102)
- Google Professional Machine Learning Engineer
- AWS Certified Machine Learning – Specialty
Benefits
- Work_model: [remote, onsite]