AI Software Engineer

AI Software Engineer

Charleston, SC

• Assist with data acquisition and collaborate with other departments for company growth via AI advancements.
• Engineer AI-driven solutions that model human behavior to accomplish complicated tasks or complete complex processes.
• Use robust statistical inference and predictive analysis on quantitative and qualitative datasets.
• Develop and maintain back-end AI systems compatible with ORBIS built devices.
• Develop and maintain front-end AI systems and interfaces.
• Actively engage in status meetings and update documentation on a regular basis via an Agile framework.

• At minimum, a bachelor’s degree in Mathematical Sciences, Data Analytics, or a Computer Science related field--MA or MS is preferred.
• US Citizen with ability to obtain Secret Security Clearance.
• Exceptional written/verbal communication along with problem-solving capabilities.
• Capable of working autonomously and collaboratively, as needed. Knowledge of version control systems such as Azure Dev Ops is preferred.
• Required skills and knowledge:
  • Programming Skills
    • Required:
      
      Python
    • Preferred: C, C#, JavaScript, etc.
  • Package Knowledge
    • Required:
      
      PyTorch, Tensorflow, Keras, Numpy, OpenCV/Pillow
    • Preferred:
      Scikit-Learn, Pandas, etc.
  • Data Structure Knowledge
    • Required:
      
      Understanding of image manipulation and normalization, ability to parse files (XML, JSON, TXT, etc.), ability to load data and model structures from files (.h5, .pt, .pickle, etc.).
    • Preferred:
      Understanding of relational vs non-relational databases.
  • Programming and Mathematics Knowledge
    • Required:
      
      Tensor (Ricci) Calculus, Graph Theory, advanced Frequentist and Bayesian statistics, object-oriented programming.
    • Preferred: systems programming, quaternions, normal vs complex spaces.
  • AI Knowledge
    • Required:
      
      Standard FFN’s, CNN’s, RNN’s and transformers, deep learning, reinforcement learning, clustering, data normalization, and data augmentation strategies.
    • Preferred:
      Prebuilt deep learning architectures, continuous learning strategies, etc.