Job Description: Edge AI Engineer
Job Title: Edge AI Engineer
Experience: 1–5 Years
Location: Hyderabad / As per business requirement
Employment Type: Full-Time
Role Overview
We are looking for an Edge AI Engineer responsible for designing, developing, optimizing, and deploying low-latency artificial intelligence solutions on edge devices and local computing environments.
The ideal candidate will have hands-on experience in deploying machine learning models on embedded platforms, optimizing AI inference performance, and working with edge computing technologies. The candidate will collaborate with AI/ML engineers, embedded engineers, data teams, and business stakeholders to build secure, scalable, and efficient edge AI solutions.
Key Responsibilities
- Design, develop, and deploy AI/ML models for edge devices and embedded platforms.
- Optimize deep learning models for low-latency inference and resource-constrained environments.
- Convert and optimize models using frameworks such as ONNX and TensorRT.
- Deploy AI models on edge hardware platforms and embedded Linux environments.
- Perform model quantization, pruning, and performance optimization techniques.
- Develop efficient inference pipelines for real-time AI applications.
- Analyze model performance, latency, memory usage, and hardware utilization.
- Integrate AI models with edge applications and device-level systems.
- Develop and maintain AI deployment workflows for edge environments.
- Troubleshoot deployment issues related to hardware, software, and model performance.
- Collaborate with data scientists and engineering teams to improve model accuracy and efficiency.
- Implement security best practices for edge AI deployments.
- Document technical designs, deployment processes, and optimization strategies.
Required Skills & Qualifications
- 1–5 years of overall experience in AI/ML, Edge AI, Embedded AI, Computer Vision, or related technologies.
- Minimum 1–2 years of hands-on experience with Edge AI development or closely related technologies.
- Strong understanding of machine learning and deep learning concepts.
- Hands-on experience with:
- ONNX model format and optimization
- TensorRT inference optimization
- Embedded Linux environments
- AI model deployment on edge devices
- Model optimization techniques
- Strong programming skills in Python and C/C++.
- Experience with deep learning frameworks such as TensorFlow, PyTorch, or similar.
- Knowledge of computer vision and real-time AI applications.
- Understanding of GPU acceleration and hardware-aware optimization.
- Experience working with AI inference pipelines and deployment workflows.
- Strong debugging and problem-solving skills.
Good to Have Skills
- Experience with edge hardware platforms such as NVIDIA Jetson, Raspberry Pi, ARM-based devices, or similar.
- Knowledge of CUDA and GPU programming.
- Experience with Docker-based deployments on edge devices.
- Familiarity with IoT systems and device communication protocols.
- Experience with MLOps practices for edge AI deployment.
- Knowledge of model compression techniques including quantization and pruning.
- Exposure to computer vision libraries such as OpenCV.
- Experience in real-time analytics and autonomous systems.
Candidate Profile Details Required
Please share the below details along with your updated resume:
Total Experience:
Relevant Experience:
Current Company:
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Expected CTC:
Notice Period:
Current Location:
Reason for Change:
Availability for Interview:
Updated Resume:
Interested candidates can share their updated profile at:
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