• Design, implement, and optimize deep learning models and pipelines

  for signal and image analysis.

• Build and maintain AI infrastructure components

  to support model training, versioning, monitoring, and deployment.

• Develop scalable data processing pipelines

  for datasets, including ingestion, labeling, augmentation, and preprocessing at scale.

• Implement automated training workflows

  using tools such as ClearML or other experiment tracking platforms, ensuring reproducibility and model governance.

• Collaborate with system engineers to integrate AI models into real-time radar systems and ensure efficient inference performance.

• Optimize distributed training and resource utilization.

• Run performance tuning, testing, and benchmarking for both training and inference workloads.

• Document infrastructure components, training procedures, and model lifecycle processes.

Required Qualifications

• 3+ years of experience in machine learning / deep learning engineering

  .

• Strong background in Python and major deep learning frameworks such as PyTorch or TensorFlow

  .

• B.Sc. in Electrical Engineering,  Computer Engineering, or a related field (M.Sc. or Ph.D. is a plus).

• Experience with AI infrastructure tooling

  , including distributed training, experiment tracking, data pipelines, and deployment automation.

• Experience with version control using Git

  , including branching strategies, collaborative workflows, and integration with CI/CD pipelines.

• Proven experience designing and implementing end-to-end training pipelines

  , including data ingestion, preprocessing, model training, evaluation, and reproducible experimentation.

• Experience with data versioning and experiment management (e.g., ClearML, MLflow, DVC

  ).