Computer Vision Engineer
4 weeks ago
Responsibilities:
Design, develop, and deploy cutting-edge AI models for edge and cloud environments, with a focus on computer vision applications.
Collaborate with cross-functional teams to understand project requirements and define AI solutions that meet business objectives.
Research and implement state-of-the-art algorithms and techniques in computer vision and deep learning.
Optimize models for inference speed, memory footprint, and power consumption on edge devices.
Develop and maintain scalable and efficient deployment pipelines for edge and cloud environments.
Evaluate and select appropriate hardware platforms and accelerators for AI model deployment.
Conduct performance analysis and debugging to ensure the reliability and accuracy of deployed models.
Stay updated with the latest advancements in AI, machine learning, and computer vision technologies.
Mentor junior engineers and provide technical guidance and support as needed.
Collaborate with product management and stakeholders to drive innovation and deliver high-quality solutions.
Technical Requirements:
Bachelor’s degree in Computer Science, Electrical Engineering, or related field. Master’s degree or Ph.D. preferred.
Minimum of 3 years of experience in AI/ML development, with a focus on computer vision applications.
Proficiency in deep learning frameworks such as TensorFlow, PyTorch, or Keras.
Strong programming skills in Python and proficiency in software development best practices.
Working Knowledge about LLMs and other GenAI architectures
Experience with edge computing platforms such as NVIDIA Jetson, Raspberry Pi, or Qualcomm Snapdragon.
Familiarity with cloud computing platforms such as AWS, Azure, or Google Cloud Platform.
Knowledge of deployment tools and technologies for edge and cloud environments (e.g., Docker, Kubernetes, AWS IoT Greengrass).
Hands-on experience with computer vision libraries and tools (e.g., OpenCV, Dlib).
Understanding of hardware-accelerated inference frameworks (e.g., NVIDIA TensorRT, Intel OpenVINO).
Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment.
Strong communication and interpersonal skills, with the ability to effectively communicate technical concepts to non-technical stakeholders.
Preferred Qualifications:
Experience with embedded systems development and optimization for AI inference.
Knowledge of edge AI frameworks such as TensorFlow Lite, PyTorch Mobile, or ONNX Runtime.
Familiarity with distributed computing and parallel processing techniques.
Publications or contributions to the AI/ML community (e.g., research papers, open-source projects).
Certification in AI/ML or related field is a plus.
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