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