8–10 years of experience in computer vision, AI/ML, or autonomous systems

Proven expertise in edge CV/AI deployment and optimization

Strong background in real-time perception and decision-making systems

Experience with UAV autonomy, GPS-denied navigation, and multi-sensor fusion

Compliance Awareness: Familiarity with aerospace/autonomy certification frameworks (DO-178C, DO-254, DGCA UAS guidelines)

Innovation: Exposure to reinforcement learning, federated learning, and edge AI optimization strategies

Reliability Engineering: Experience in robustness testing, redundancy, and survivability under mission conditions

Leadership: Ability to mentor teams, manage cross-functional collaboration, and drive system-level reviews

• Programming: Python, C++
• Machine Learning: PyTorch
• Deployment: Docker
• Middleware: ROS / ROS 2

Bachelor’s / Master’s in Computer Science / AI / Robotics

• Edge AI Architecture
  • Define end-to-end CV/AI architecture for edge deployment
  • Select hardware platforms (NVIDIA Jetson, Edge TPU, Hailo accelerators)
  • Optimize SWaP-C (Size, Weight, Power, Cost) for mission readiness
• Advanced Perception & Navigation
  • Lead development of Visual SLAM and Visual-Inertial Odometry (VIO)
  • Implement GPS-denied navigation strategies
  • Integrate multi-sensor fusion (vision, thermal, LiDAR)
• Real-Time Systems
  • Define real-time video processing pipelines
  • Ensure low-latency perception and decision-making
• Targeting & Mission Systems
  • Develop target acquisition and lock-on strategies
  • Support advanced mission applications (ISR systems, loitering munitions)
• MLOps & Lifecycle
  • Define MLOps pipelines and model lifecycle management
  • Ensure continuous training, validation, and deployment workflows
• Leadership & Integration
  • Lead PDR/CDR software reviews and sign-offs
  • Coordinate with GNC, payload systems, and avionics teams for integration