ML Engineer

Responsibilities.

• Design and fine-tune PyTorch deep learning models for Computer Vision and Time Series/rPPG.
• Own the end-to-end ML lifecycle from data preparation and training to deployment and monitoring.
• Build ML pipelines using Python, MLflow, and Azure ML.
• Deploy and optimise models on CPU/GPU cloud endpoints for low-latency inference.
• Implement model validation, bias testing, drift monitoring, and performance monitoring.
• Integrate ML models with REST/WebSocket APIs and backend/mobile systems.
• Use AI coding assistants and AI agents to accelerate ML development and experimentation.

Requirements:

• Strong Python + PyTorch with hands-on Deep Learning.
• Computer Vision: classification, segmentation, detection, CNNs, Transformers, U-Net.
• Time Series: signal processing, rPPG, physiological signals.
• MLflow + Azure ML for experiment tracking and model deployment.
• Production ML model deployment, REST APIs, cloud/MLOps.
• Experience with AWS/Azure/GCP and AI coding tools.

Must-Have:

• 3+ years ML experience, including 1+ year production ML deployment.
• Production experience in Computer Vision and/or Time Series.
• Strong ownership of the end-to-end ML lifecycle.
• Knowledge of data privacy/PHI, HIPAA, ISO 27001 or GDPR.
• Relevant degree in CS, Engineering, Mathematics, Physics, or equivalent.