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AI Software Engineer

2 months ago

West Columbia, United States Orbis Corporation Full time
AI Software Engineer

Charleston, SC

Responsibilities:
  • Assist with data acquisition and collaborates with other departments for company growth via AI advancements.
  • Engineer AI-driven solutions that model human behavior to accomplish complicated tasks or to 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.
Requirements:
  • 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 DevOps, is preferred.
  • Below are the required skills and knowledge:
    • Programming Skills: - Required: Python - Additionally Preferred: C, C#, JavaScript, etc.
    • Package Knowledge: - Required: PyTorch, Tensorflow, Keras, Numpy, OpenCV/Pillow. - Additionally Preferred: Scikit-Learn, Pandas, etc.
    • Data Structure Knowledge: - Required: Understanding of image manipulation and normalization, ability to parse file formats (XML, JSON, TXT, etc.), ability to load data and model structures from files (.h5, .pt, .pickle, etc.). - Additionally 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. - Additionally 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. - Additionally Preferred: Prebuilt deep learning architectures, continuous learning strategies, etc.