Deep Learning Engineer Healthcare AI

Deep Learning Frameworks: Proficient in PyTorch or TensorFlow
Programming: Strong Python skills with experience in NumPy, Pandas, and scikit-learn
Model Development: Experience with CNNs, RNNs, transformers, and self-supervised learning
Healthcare Data Handling: Exposure to medical images (e.g., DICOM), tabular EHR, or time-series signals
MLOps Basics: Knowledge of model versioning, deployment tools (e.g., ONNX, Docker), and performance monitoring
Problem-Solving Mindset: Ability to build practical solutions in resource-constrained, noisy data environments
Bonus: Familiarity with medical standards (e.g., ICD codes, HL7) or experience in Kaggle/healthcare AI competitions
Key Responsibilities
Design and train deep learning models for medical imaging, diagnostics, and predictive healthcare analytics
Collaborate with clinicians, product teams, and data scientists to translate healthcare problems into AI solutions
Work with real-world healthcare data (images, time-series, EHR), ensuring regulatory compliance (e.g., HIPAA/GDPR)
Contribute to model deployment pipelines, optimizing performance in production environments
Role: Machine Learning Engineer
Industry Type: IT Services & Consulting
Department: Data Science & Analytics
Employment Type: Full Time, Permanent
Role Category: Data Science & Machine Learning

UG: Any Graduate
PG: Any Postgraduate