5–7 years of experience in computer vision and AI/ML engineering

Strong knowledge of deep learning algorithms, real-time systems, and edge deployment workflows

Experience with multi-object detection, tracking, and situational awareness systems

Exposure to UAV autonomy, EO/IR data processing, and precision landing algorithms

Innovation: Exposure to reinforcement learning, SLAM, and sensor fusion for UAV autonomy

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

Leadership: Ability to mentor junior engineers and lead computer vision design reviews

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

• ML Frameworks: PyTorch, TensorFlow
• Optimization Tools: TensorRT, CUDA
• Middleware: ROS / ROS 2
• Video/Data Pipelines: GStreamer
• Strong debugging, profiling, and documentation skills

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

• Deep Learning & Optimization
  • Develop and optimize deep learning models (quantization, pruning)
  • Accelerate inference using TensorRT and CUDA
• Detection & Tracking
  • Implement multi-object detection (YOLO-based models)
  • Develop tracking algorithms (DeepSORT) and classification pipelines
• Vision & Navigation Algorithms
  • Implement visual odometry and precision landing algorithms
  • Work with EO/IR data for robust perception and situational awareness
• Video & Data Pipeline
  • Optimize real-time video streaming pipelines using GStreamer
  • Generate synthetic datasets for training and validation
• Edge Deployment
  • Deploy models on edge hardware (Jetson or equivalent)
  • Perform profiling and optimization for real-time performance