AI Engineer

Armada Armada · Enterprise · Bellevue, Sunset Corporate · R&D - AI Engineering

Armada is seeking an AI Engineer to build, evaluate, and deploy ML/DL models, particularly transformers, for real-time computer vision and other applications in industrial settings. The role involves data preparation, model deployment in production, and establishing continuous learning pipelines.

What you'd actually do

  1. Translating business requirements into requirements for AI/ML models.
  2. Preparing data to train and evaluate AI/ML/DL models.
  3. Building AI/ML/DL models by applying state-of-the-art algorithms, especially transformers. In some cases, leverage existing algorithms from academic or industrial research.
  4. Testing, evaluating the AI/ML/DL models, benchmarking their quality, and publishing the models, data sets, and evaluations.
  5. Deploying the models in production by containerizing the models.

Skills

Required

  • Python
  • Java
  • C/C++
  • containers
  • numeric libraries
  • modular software design
  • statistical machine learning techniques
  • deep-learning
  • natural language processing modeling
  • supervised learning
  • unsupervised learning
  • transfer learning
  • machine learning techniques and algorithms
  • DNN architectures (Transformers, CNN, R-CNN, RNN, BERT, GAN, autoencoders, etc.)
  • PyTorch
  • Tensorflow

Nice to have

  • building, programming, and integrating software and hardware for autonomous or robotic systems
  • computationally efficient software for real-time requirements
  • Kubernetes
  • analytical skills
  • time-management
  • organization skills
  • teamwork
  • interpersonal skills

What the JD emphasized

  • hands-on expertise across a range of domains, including real-time computer vision, statistical machine learning, natural language processing, transformers, control and navigation, reinforcement learning, and large-scale distributed AI systems
  • strong skills in machine learning (ML), deep learning (DL), and real-time computer vision techniques
  • building ML/DL models
  • deploying solutions in production environments
  • independently deploy ML models into production
  • cutting-edge AI
  • thrilling AI and ML challenges
  • disruptive edge-compute systems capable of autonomous learning, prediction, and adaptation using vast, real-time datasets
  • self-driving cars, camera networks, robotics, drones, conversational agents, and real-time monitoring and diagnostic systems
  • state-of-the-art algorithms, especially transformers
  • state-of-the-art DNN architectures (Transformers, CNN, R-CNN, RNN, BERT, GAN, autoencoders, etc.)

Other signals

  • deploying solutions in production environments
  • building ML/DL models
  • real-time computer vision
  • transformers
  • reinforcement learning
  • large-scale distributed AI systems