Tech Lead - Computer Vision
Indexed description
This role involves designing robust software architectures, integrating advanced algorithms, and optimising solutions for real-world UAV applications such as Vision based UAV navigation, object detection/tracking, obstacle avoidance, etc. In some cases this would require processing of not just camera but also other kinds of sensors such as radar, inertial, GPS, etc. Prior experience in developing such multi-modal architectures for applications such as Advanced Driver Assistance Systems (ADAS), Robotics or UAVs would be highly preferred.
Key Requirement & Responsibilities:
Deep Learning and Computer Vision Expertise:
- Lead the incorporation of advanced DL/CV algorithms into system architecture.
- Stay abreast of the latest trends and innovations in computer vision technology.
- Develop and articulate a clear software architecture for computer vision systems.
- Design scalable and modular software systems that align with business objectives.
- Develop strategies for leveraging hardware capabilities to optimise software performance.
- Collaborate with hardware teams to align software and hardware development efforts.
- Define strategies for optimising computer vision and deep learning inference on edge devices.
- Architect solutions for edge computing deployment, ensuring optimal performance on resource-constrained devices.
- Address challenges related to latency and real-time processing.
- Collaborate with cross-functional teams, including hardware engineers, software developers, and researchers.
- Facilitate effective communication and coordination among team members.
- This position is open for Navi Mumbai OR Bangalore Locations
- Expertise in Visual Inertial SLAM.
- Experience in edge computing and deploying solutions on embedded devices.
- Knowledge of ROS/ROS2 and hardware acceleration using OpenCL/CUDA is a plus.
- Publications in top CV/ML conferences is a plus.
- Understanding of software architecture principles and best practices.
- Excellent leadership and communication skills.
- Bachelor''s or Master''s degree in Computer Science, Electrical Engineering, or a related field.
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