Futuremug
Location
Kochi | India
Job description
1. Develop and implement AI algorithms and models to analyse healthcare data, including electronic health records (EHRs), medical imaging data, wearable sensor data, and genomic data.
2. Design and optimize machine learning pipelines for data pre-processing, feature extraction, model training, and evaluation, ensuring scalability, performance, and reliability.
3. Collaborate with clinicians, healthcare professionals, and domain experts to understand clinical requirements, validate AI models, and integrate AI-driven solutions into clinical workflows.
4. Conduct exploratory data analysis, identify patterns, trends, and insights in healthcare data, and translate findings into actionable recommendations and decision support tools.
5. Explore and experiment with emerging AI technologies, frameworks, and tools to drive innovation and continuous improvement in AI capabilities.
6. Ensure compliance with regulatory requirements, data privacy standards, and ethical guidelines related to AI applications in healthcare, including HIPAA and GDPR.
7. Collaborate with software engineers, data scientists, and other stakeholders to deploy AI models into production environments, monitor performance, and iterate on solutions based on feedback.
8. Participate in code reviews, knowledge sharing sessions, and cross-functional team meetings to foster collaboration, learning, and best practices in AI development.
9. Stay abreast of the latest advancements in AI, machine learning, and health tech by actively participating in conferences, workshops, and professional development activities.
10. Contribute to the research community by publishing papers, presenting findings at conferences, and participating in open-source projects related to AI in healthcare.
1. Bachelor's degree or higher in computer science, engineering, mathematics, statistics, or a related field. Advanced degrees (Master's or Ph.D.) are preferred.
2. Minimum of 5 years of experience in AI engineering, machine learning, or data science, with a focus on healthcare applications.
3. Proficiency in programming languages commonly used in AI and machine learning, such as Python, R, Java, or C++.
4. Strong understanding of machine learning algorithms, statistical modelling, and data analysis techniques.
5. Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.
6. Knowledge of healthcare data sources, formats, and standards, including electronic health records (EHRs), medical imaging data, and clinical terminology.
7. Experience with cloud computing platforms (e.g., AWS, Azure, Google Cloud) and distributed computing frameworks.
8. Excellent communication skills, with the ability to effectively communicate technical concepts to non-technical stakeholders.
9. Ability to work in a fast-paced, collaborative environment, adapt to changing priorities, and prioritize tasks effectively.
10. Commitment to ethical and responsible AI practices, including fairness, transparency, accountability, and bias mitigation.
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