Job Title

Overview mlHealth 360 is Health-tech company. We are committed to revolutionizing and enable the healthcare industry by providing innovative tools and resources that empower healthcare professionals and institutions. Our current focus is around leveraging the power of deep learning technology for image screening to improve patient outcomes, reduce costs, and increase efficiency. Role Description As an ML Engineer at mlHealth 360 , you will be a hands-on technical driver responsible for the end-to-end development of our AI-powered medical diagnostic tools. This is a hybrid role for a practitioner who is equally comfortable writing research-grade deep learning code and production-grade data infrastructure. You will be responsible for building, training, and deploying high-performance models that directly impact patient care, moving seamlessly between R&D experimentation and robust engineering execution. Responsibilities Design, implement, and train deep learning models (specifically for image segmentation and classification) using PyTorch or TensorFlow. Build and maintain end-to-end data pipelines to ingest, process, and version large-scale medical imaging datasets (DICOM/NIfTI). Wrap models into scalable microservices using Docker and Kubernetes, ensuring low-latency inference in clinical environments. Clean, augment, and manage high-dimensional radiological data, working directly with medical annotation tools and clinical datasets. Profile and optimize model performance (quantization, pruning, or hardware acceleration) for efficient deployment on cloud or edge infrastructure. Code the interfaces and APIs required to integrate AI outputs into clinical workflows and web-based UI ecosystems. Implement MLOps practices, including automated testing, model monitoring, and continuous retraining (CT) loops. Required Skills and Qualifications 3+ years of hands-on experience building and deploying machine learning models, with a strong emphasis on productionizing research. 1+ years of experience in medical image analysis, with deep technical knowledge of CT, MRI, or X-ray data structures and radiological workflows. Expert-level Python skills with a focus on writing high-performance, maintainable code. Extensive experience with PyTorch, TensorFlow, or Keras, specifically applied to computer vision and volumetric data. Practical, hands-on experience with Docker, Kubernetes, and ML orchestration tools (e.g., MLflow, Kubeflow, or Prefect). Direct experience managing cloud-based training environments (AWS, Azure, or GCP) and optimizing GPU utilization. Understanding of data privacy and security requirements (HIPAA/PIPEDA) as they relate to engineering architecture. Education Masters or Ph.D. in Computer Science, Biomedical Engineering, Data Science, or a related fieldwith a demonstrable portfolio of shipped machine learning products. #J-18808-Ljbffr