We are looking for an experienced Deep Learning & AI Infrastructure Engineer to join our AI team. In this role, you will develop and deploy advanced deep learning models for signal processing and build the infrastructure and tooling required to scale AI training and inference in both research and production environments.

You will be responsible for the end-to-end lifecycle of AI workloads, including data ingestion and preprocessing pipelines, model training infrastructure, efficient inference systems, and automated experiment tracking.

Key Responsibilities

• 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.
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• 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.
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• Optimize distributed training and resource utilization.
• Run performance tuning, testing, and benchmarking for both training and inference workloads.
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• Document infrastructure components, training procedures, and model lifecycle processes.

Required Qualifications

• 3+ years of experience in machine learning / deep learning engineering.
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• 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).
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• Experience with AI infrastructure tooling, including distributed training, experiment tracking, data pipelines, and deployment automation.
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• 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).