[Remote] AI Engineer (Healthcare SaaS)- Medicat
Note: The job is a remote job and is open to candidates in USA. Banyan Software is a company dedicated to acquiring and growing software businesses in specialized industries. They are seeking an experienced AI Engineer to design and implement AI-native capabilities within their healthcare SaaS platform, MedicatOne, focusing on building production-grade AI systems that enhance healthcare operations.
Responsibilities
- Design and implement AI-powered capabilities within the MedicatOne platform
- Integrate modern AI models and services into production workflows
- Develop systems that interact with structured application data and APIs
- Ensure AI features are reliable, performant, and secure
- Work with large datasets related to healthcare operations
- Design systems that leverage structured data effectively
- Build logic that enables intelligent interpretation of operational data
- Collaborate with engineering on data pipelines, schemas, and analytics frameworks
- Integrate AI capabilities into existing SaaS architecture
- Work with backend engineers to ensure scalable system design
- Optimize performance of data-intensive AI workflows
- Contribute to platform-level architecture decisions
- Ship production-ready AI features quickly and iteratively
- Collaborate closely with leadership on prioritization and implementation
- Help establish best practices for AI development within the organization
Skills
- 5+ years building production SaaS systems
- Direct experience building and implementing AI/ML or LLM-based features
- Strong experience working with structured data and analytics systems
- Experience working with healthcare data or healthcare SaaS environments, including familiarity with the regulatory, privacy, and compliance considerations that shape technical decisions (HIPAA, PHI handling, de-identification)
- Demonstrated ability to build and deploy systems end-to-end
- Working knowledge of modern AI infrastructure and patterns, including: LLM API integration (OpenAI, Anthropic, or similar) in production environments
- Retrieval-Augmented Generation (RAG): designing retrieval pipelines, chunking strategies, and grounding LLM responses in real data
- Vector databases: embedding, indexing, and querying unstructured or semi-structured data
- Data modeling and SQL
- Data pipelines and analytics workflows and working with large structured datasets
- API design and backend services
- Working with large, structured datasets
- Experience with healthcare informatics standards or healthcare data environments
Company Overview