Forward Deployed Engineer - Applied AI
Banyansoftware · United States
About this role
**Banyan Software — Forward Deployed Engineer (Applied AI)** ## About the role Medicat builds **MedicatOne**, the EHR used by college health and counseling centers across the US. Banyan is investing in **AI that reaches clinicians and students in production**. This is an **engineering role first**. You’ll write production code most days—**LLM integrations, automations, data workflows, and the APIs** that connect them to MedicatOne. You’ll also work closely with clinic customers and internal teams, turning what you see in real workflows into working software—**often in days, not quarters**. If you want to be the person who **ships features, debugs with customers, and hardens for HIPAA**, keep reading. ## What you’ll do - **Build and ship AI features end to end**: design, code, test, and deploy LLM-powered features in MedicatOne (prompt design, tool calling, retrieval, and evaluation harnesses). - **Pick the simplest reliable solution**: sometimes that’s an LLM; often it’s plain code. - **Integrate with real clinical data**: build services, SQL, and data pipelines to move PHI safely between MedicatOne, customer systems, and model providers. - **Deploy alongside customers**: join clinic implementation/discovery calls as the technical lead, scope what can be built, prototype against their data, and stay through go-live. - **Embed with internal teams**: find and automate repetitive work; help analysts and developers get the most out of AI tools. - **Own production quality**: instrument, monitor, and debug (latency, cost, accuracy, failure modes); write evals to catch regressions before customers see them. - **Make AI safe for healthcare**: implement HIPAA-compliant patterns for PHI handling, logging, access control, de-identification, and vendor data boundaries. - **Harden internal tools**: review and productionize AI tools built by non-engineering teams before they touch customer data. - **Feed the roadmap with evidence**: bring back what you learn as working prototypes and technical recommendations. **Time split:** ~**70% hands-on engineering** / ~**30% embedded with customers and internal teams**. ## What you bring - **3+ years as a software engineer** shipping production code in **B2B SaaS** (IC track record). You still write code every week. - Strong in at least **one backend language**: **Python**, **C#/.NET**, or **TypeScript/Node** (comfortable reading the others). - **Hands-on LLM API experience**: shipped production features on LLM APIs (OpenAI, Anthropic, Azure OpenAI), including prompt design and function/tool calling—and know when not to use LLMs. - **End-to-end system design**: production systems from the ground up (data flow, security, multi-tenancy). - **Solid data skills**: production SQL, schema design, and data pipelines for relational databases. - **API and integration experience**: REST API design, authentication, webhooks, and third-party integrations. - **Production engineering habits**: Git, code review, automated testing, CI/CD, and deploying to a major cloud (**Azure or AWS**). - **Regulated/sensitive data experience**: built software handling **PHI/PII** (or similarly protected data) with required access controls and audit trails. - **Comfort with customers and colleagues**: can explain trade-offs to clinic leadership/analysts, then build. - **Authorized to work in and resides in the US** (no visa sponsorship). ## Nice to have - **RAG/embeddings/vector databases** (pgvector, Pinecone, etc.) and judgment on w
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