AI Software Development Engineer

AI Software Development Engineer

Education:

  • Bachelor's degree (or higher) in Computer Engineering, Software Engineering, Electrical-Electronics Engineering, Mechatronics Engineering, Aerospace Engineering, Mathematics, or related fields.

Language:

  • English, Urdu

Computer Skills:

  • Proficiency in programming languages such as Python, C++, and MATLAB for AI/ML development, Experience with image processing and computer vision libraries like OpenCV, NumPy, and MATLAB Computer Vision Toolbox, Familiarity with software lifecycle tools (e.g., Git, Jira, DOORS) and clean code principles using OOP design patterns, MS Office Applications. 

Experience & Abilities:

•  1 - 3 years of experience in machine learning, including data preparation, labeling, model training, testing, and deployment.
• Experience with deep learning frameworks like TensorFlow, Keras, PyTorch, and tools for data analysis/visualization.
• Knowledge in advanced areas: computer vision (e.g., object detection, SLAM, visual odometry), reinforcement learning, CNNs, neural networks, NLP, signal processing, big data, and data fusion/mining.
• Background in embedded/real-time systems, probability-based approaches, data engineering (NoSQL/SQL databases, data flow systems), and synthetic data generation.
 

Job Description / Responsibilities:

  • AI/ML Development & Integration: Create and deploy algorithms for UAV autonomy in flight/mission control, using computer vision, deep/reinforcement learning, and rule-based systems.
  • Architecture Design: Build intelligent platforms with embedded Linux, comms, UIs, data/multimedia management for tailored AI solutions.
  • Data & Model Handling: Curate/label datasets; train/test models via PyTorch/TensorFlow; analyze big data, signals, and generate synthetic visuals.
  • Research & Validation: Review emerging tech; prototype/test algorithms (e.g., SLAM, object detection) in real-time, report/optimize results.
  • Collaboration & Implementation: Partner on data fusion, image processing, and integration; guide projects from R&D to deployment.
  • Testing & Standards: Automate tests, oversee version control, and uphold clean code/lifecycle practices for defense tech advancement.