Edge AI Engineer

Job Description: Edge AI Engineer Job Title: Edge AI Engineer Experience: 15 Years Location: Hyderabad / As per business requirement Employment Type: Full-Time Role OverviewWe 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 ResponsibilitiesDesign, 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 & Qualifications15 years of overall experience in AI/ML, Edge AI, Embedded AI, Computer Vision, or related technologies.Minimum 12 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 optimizationTensorRT inference optimizationEmbedded Linux environmentsAI model deployment on edge devicesModel optimization techniquesStrong 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 SkillsExperience 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 RequiredPlease share the below details along with your updated resume: Total Experience: Relevant Experience: Current Company: Current CTC: Expected CTC: Notice Period: Current Location: Reason for Change: Availability for Interview: Updated Resume: Interested candidates can share their updated profile at: [HIDDEN TEXT] Job Description: Edge AI Engineer Job Title: Edge AI Engineer Experience: 15 Years Location: Hyderabad / As per business requirement Employment Type: Full-Time Role OverviewWe 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 ResponsibilitiesDesign, 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,