OSP Labs

AI/ML Responsibilities :

- Develop, deploy, and maintain scalable AI/ML solutions that align with healthcare industry requirements and customer needs.

- Work closely with data engineers and analysts to preprocess, clean, and structure healthcare data for ML applications.

- Design and implement machine learning models to solve real-world healthcare problems, such as patient readmission prediction, disease progression modelling, and medical image analysis etc.

- Integrate AI/ML solutions into production environments, ensuring models are efficient, reliable, and aligned with client objectives.

- Collaborate with healthcare clients to define project requirements and KPIs, ensuring that all solutions add measurable value.

- Stay informed on the latest advancements in AI/ML, particularly in healthcare applications, and integrate new techniques to enhance our solutions.

- Ensure all AI/ML applications comply with healthcare regulations (HIPAA, GDPR, etc.) and uphold high standards for patient data privacy and security.

- Monitor and fine-tune models post-deployment to ensure they perform as expected and adapt to evolving healthcare data.

Requirements :

- 5+ years of experience in machine learning, deep learning, and AI applications, preferably within the healthcare industry.

- Proficiency in Python and/or R, and experience with ML libraries and frameworks such as TensorFlow, PyTorch, Scikit-Learn, and others.

- Strong understanding of healthcare data types and sources, such as EHR/EMR data, claims data, imaging data, and genomics.

- Familiarity with cloud platforms (AWS, Azure, Google Cloud) and deploying ML models in cloud-based environments.

- Experience with healthcare-specific machine learning use cases (e.g., patient outcomes prediction, medical image classification, NLP for clinical notes).

- Strong understanding of healthcare privacy and regulatory standards (HIPAA, GDPR).

- Excellent problem-solving, analytical, and communication skills.

- Ability to work collaboratively in a cross-functional team and translate complex technical concepts for non-technical stakeholders.

(ref:hirist.tech)

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