Join a global organization dedicated to improving health outcomes through technology. This role focuses on advancing health optimization by developing and deploying cutting-edge AI/ML solutions. You'll contribute to a culture of inclusion and innovation, working alongside talented peers to make a real impact.

This is an opportunity to be part of a team that's transforming healthcare, connecting people with the resources they need to live healthier lives. Embrace a collaborative environment where your contributions are valued and career development is encouraged. Start your journey with us to Care, Connect, and Grow together.

Key responsibilities include designing, developing, and deploying machine learning models for predictive analytics, forecasting, and recommendation systems. You will translate business needs into scalable AI/ML solutions, conduct exploratory data analysis, and perform feature engineering.

Build and maintain robust machine learning pipelines for data ingestion, model training, validation, and deployment. Implement model evaluation processes, support hyperparameter tuning, and contribute to repeatable AI engineering practices. Deploy models using APIs, containerized services, and cloud platforms, ensuring MLOps principles are followed.

Monitor deployed models for accuracy, drift, and latency, and support troubleshooting and root cause analysis. Collaborate with data engineering teams to develop scalable data pipelines and integrate AI capabilities into enterprise applications. Adhere to Responsible AI practices, including explainability, fairness, and governance, and explore emerging AI technologies like LLMs and RAG.

A Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, Artificial Intelligence, or a related field is required. The role demands 5+ years of experience in Machine Learning, Artificial Intelligence, Data Science, or Software Engineering, with a proven track record of developing and deploying ML solutions in enterprise or cloud environments.

Proficiency in Python and SQL is essential. You should have experience building ML pipelines, working with APIs, cloud AI services, and distributed data platforms. A solid understanding of statistical modeling, predictive analytics, model evaluation, and feature engineering is expected. Familiarity with MLOps practices, including model deployment and monitoring, is also necessary.

Candidates must possess strong analytical, problem-solving, communication, and collaboration skills. A good grasp of Responsible AI principles, model governance, and compliance requirements is also important for this position.