5–7 years of experience in AI, ML, robotics, or autonomy engineering

Strong knowledge of ML algorithms for perception, decision-making, and navigation

Experience with UAV navigation systems and sensor fusion (GPS, IMU, camera)

Exposure to simulation environments such as AirSim, Gazebo, or equivalent

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

Innovation: Exposure to reinforcement learning, edge AI, and federated learning for UAV autonomy

Leadership: Ability to mentor junior AI/ML engineers and lead design reviews

Reliability Engineering: Experience in robustness testing, fault tolerance, and redundancy strategies

• Programming: Python
• ML Frameworks: TensorFlow, PyTorch
• Version Control: Git
• Simulation Tools: AirSim, Gazebo
• Strong debugging, optimization, and documentation skills

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

• Algorithm Development
  • Design ML algorithms for perception and decision-making
  • Develop obstacle avoidance models and object detection systems
  • Train and validate models using TensorFlow and PyTorch
• Navigation & Planning
  • Implement adaptive path planning and route optimization algorithms
  • Integrate ML algorithms with flight dynamics and control systems
• Simulation & Testing
  • Develop and test algorithms in simulation environments (AirSim, Gazebo)
  • Validate system performance across diverse mission scenarios
• Real-Time Optimization
  • Optimize models for real-time execution on embedded hardware
  • Improve latency, efficiency, and robustness of deployed systems
• Situational Awareness
  • Develop multi-sensor situational awareness systems
  • Fuse data from GPS, IMU, and camera for reliable autonomy