Manager of Applied AI Products

Banyan Software

2d•$120,000 - $145,000•Remote

About The Position

This is a player/coach role. The right candidate is someone who will personally design, build, and ship AI capabilities while also influencing strategy, engaging clients, and elevating the team around them. This role requires genuine fluency in clinical workflows, healthcare data standards, and the compliance constraints that govern systems handling protected health information in addition to the hands-on technical depth to turn that domain knowledge into AI-powered product capabilities that providers actually use. Medicat is the leading EHR software provider for college health and counseling, serving hundreds of institutions across the United States. Our platform, MedicatOne (M1), supports practice management, documentation, workflow automation, and the student experience for health and counseling clinics nationwide. We are investing in AI-driven capabilities that meaningfully change how healthcare providers interact with their data, clinical workflows, and operational systems, and we are looking for a leader who can help us do that with the credibility and context that comes from deep healthcare IT experience.

Requirements

Must currently reside in the United States
Must be authorized to work in the US without visa sponsorship; Medicat does not offer visa sponsorship now or in the future
Meaningful experience in healthcare SaaS, health systems, or digital health, ideally including time working with or inside an EHR environment
Working knowledge of healthcare data standards: HL7 v2, FHIR (R4 preferred), CCD/CCDA, and common interoperability patterns
Familiarity with clinical workflow design: how providers document, order, and communicate within an EHR context
Direct experience navigating HIPAA compliance, PHI handling, de-identification, and the privacy constraints that shape system design in regulated healthcare environments
6+ years in product engineering, technical product management, or a hands-on hybrid role in B2B SaaS with at least 3 years personally building AI or ML-powered features in production
A portfolio of AI work you built yourself; we will ask about the systems you designed and the code you wrote, not just the teams you managed
Demonstrated success contributing to a product roadmap end-to-end: from strategy through personal delivery and client adoption
Direct client-facing experience and comfortable presenting to health system or clinic leadership, running discovery sessions, and handling objections
Hands-on experience with LLM API integration (OpenAI, Anthropic, or similar) in production healthcare or enterprise environments
Working knowledge of RAG architectures: retrieval pipeline design, chunking strategies, grounding, and evaluation
Familiarity with vector databases, embedding models, and unstructured data indexing
Comfortable with data modeling, SQL, and analytics workflows over large structured healthcare datasets
API design and backend services experience sufficient to review and guide engineering decisions
Able to operate across the strategic and tactical; equally at home in a roadmap session with the CTPO and a technical design review with engineers
Strong written and verbal communication; can synthesize complex AI and clinical concepts for provider, operational, and executive audiences

Nice To Haves

Exposure to college health, student health, or behavioral health contexts is a strong plus

Responsibilities

Partner closely with the MedicatOne Product Manager to shape the AI product roadmap, contributing deep technical and domain expertise to prioritization decisions
Translate provider pain points, EHR workflow gaps, and healthcare data challenges into well-scoped AI product proposals
Collaborate with the CTPO and product leadership to inform the AI vision for the platform
Establish evaluation frameworks for AI feature performance, clinical utility, and adoption
Serve as a senior point of contact for strategic client conversations about AI capabilities and roadmap
Lead structured discovery with health center directors, clinicians, and administrators to surface unmet needs and validate product direction
Partner with Customer Success and Sales to translate client feedback into product requirements
Represent Medicat's AI strategy in prospect conversations, RFP responses, and industry forums when needed
Personally design and build AI features
Write production-quality code for LLM integrations, RAG pipelines, vector search, and data workflows
Own the architecture of AI systems end-to-end: from data ingestion and retrieval design through API contracts and deployment
Ensure implementations are reliable, HIPAA-compliant, and built to scale and be accountable for that quality yourself
Stay current on the AI landscape and bring relevant patterns and tools into the platform with your own hands
Work alongside engineers and product teammates to drive features from concept to launch as a contributor, not just a coordinator
Share what you know: raise the technical bar for others working on AI-adjacent problems through code review, pairing, and direct feedback
Align stakeholders on AI priorities, tradeoffs, and timelines with the technical credibility that comes from being in the work yourself
Help establish best practices for responsible, compliant AI development that the whole team can follow
Act as an internal resource for other departments looking to use AI effectively; help teams across the company identify where AI adds real value and how to apply it responsibly
Provide hands-on guidance to non-engineering teams building AI-assisted workflows or tools, ensuring they follow sound practices from the start
Serve as the final technical reviewer for any AI-built tools or automations created by non-technical employees before they are used in production and evaluate for reliability, security, PHI exposure, and compliance risk
Establish and maintain a lightweight review process for employee-created AI tools so that quality and safety checks are consistent across the organization
Develop and maintain recommendations for company-wide AI acceptable use policies, covering what tools employees may use, how data may be shared with AI systems, and what classes of use require review or approval.
Help build AI literacy across Medicat through documentation, working sessions, or direct support