Senior AI Engineer - Healthcare AI, Remote

Sharecare; Lansing (MI); Remote; USD 120,000 - 190,000; Full time

Job summary

Sharecare is seeking an AI Engineer to drive the transformation into the era of agentic engineering. You will design, develop, and deploy AI/ML solutions across architecture, data, and governance, partnering with stakeholders to translate business needs into scalable AI systems.

You will own end-to-end systems, from factory spine to data pipelines, ensuring safety, auditability, and compliance while advancing healthcare data products using LLMs, RAG, and production-grade ML practices.

Qualifications

• Bachelor's degree in a relevant field.
• Minimum 4 years of software engineering or systems architecture experience.
• Experience with ML models, LLMs, and AI-driven applications.
• Cloud experience (Azure, AWS, GCP) and CI/CD pipelines.

Responsibilities

• Design, develop, test, deploy, and maintain AI/ML solutions.
• Develop and optimize ML models, analytics, and automation tools.
• Implement generative AI applications using LLMs and RAG.
• Collaborate with stakeholders to translate requirements into AI-driven solutions.
• Integrate AI models into enterprise workflows and data pipelines.
• Monitor model performance and manage lifecycle and governance.

Skills

AI/ML architecture

Cloud platforms

MLOps / DevOps

CI/CD pipelines

Containerization

NLP / LLMs

Data privacy & security

Problem solving

Team collaboration

Education

Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Mathematics, Statistics, Information Technology, or a related field

Tools

CI/CD pipelines

Containerization

MLOps platforms

Job description

Sharecare is seeking an AI Engineer to drive the transformation into the era of agentic engineering. You will design, develop, and deploy AI/ML solutions across architecture, data, and governance, partnering with stakeholders to translate business needs into scalable AI systems.

You will own end-to-end systems, from factory spine to data pipelines, ensuring safety, auditability, and compliance while advancing healthcare data products using LLMs, RAG, and production-grade ML practices.