Lead Machine Learning Engineer

Key Responsibilities:
Lead the development and deployment of machine learning (ML) and data science solutions focused on medical use cases, including MRI and other healthcare datasets.
Research and implement appropriate ML algorithms and tools for healthcare-related tasks.
Develop and deliver end-to-end ML applications based on business and clinical requirements.
Lead and manage multiple machine learning projects simultaneously, ensuring they are completed on time, within scope, and meet the quality standards.
Develop detailed project plans, assign resources, and monitor progress.
Coordinate cross-functional teams and stakeholders to ensure project alignment with business objectives.
Perform machine learning experiments, run tests, fine-tune models, and continuously improve performance based on experimental results.
Train, retrain, and deploy ML systems based on new data and clinical feedback.
Extend and optimise existing ML libraries, frameworks, and pipelines.
Perform statistical analysis to extract key insights from medical data and apply findings to improve ML models.
Monitor, evaluate, and systematise the performance of deployed ML models using quality assurance and monitoring frameworks.
Collaborate on research and contribute to technical proposals that drive the growth and direction of artificial intelligence research.
Guide and mentor team members in ideation, development, and the delivery of ML products.
Build ML pipelines using active learning, online learning, and reinforcement learning techniques, with a focus on optimising models based on continuous clinical feedback.
Contribute to the development of traditional/statistical ML models and custom libraries/tools required to address unique computational neuroscience challenges.
Ensure that all solutions comply with healthcare and medical data regulations such as HIPAA, ISO, and FDA guidelines for medical devices.
Collaborate with legal and compliance teams to ensure that systems meet data security, privacy, and ethical standards.
Qualifications:
Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Machine Learning, or a related field. PhD preferred.
5+ years of experience in designing and deploying machine learning solutions in healthcare or related industries.
Strong programming skills in Python, C++, and/or Matlab, with experience in deep learning frameworks like PyTorch.
Proven experience with medical datasets (MRI, EEG, CT, etc.) and familiarity with medical image processing.
Experience with cloud platforms (AWS, GCP, Azure) and scalable ML infrastructure.
Proficiency in ML libraries and tools (e.g., scikit-learn, XGBoost) and experience with big data technologies and tools like Spark, Celery, Kafka, Cassandra, etc.
Knowledge of active, online, or reinforcement learning methodologies is a plus.
Experience with MLOps and building scalable and large-scale image processing and deep learning pipelines.
Experience with LLMs and Transformers are also preferred.
Experience with model monitoring, A/B testing, and continuous deployment of ML systems.
Excellent communication and leadership skills to guide technical teams and collaborate with stakeholders.
Strong research background with contributions to scientific publications or participation in healthcare-related AI research.