Senior Applied AI Engineer - Operate

Vi Operate puts AI agents into the daily operations of large health systems, providers, and pharma organizations. Our agents talk to patients and providers in real time over the phone, read the documents those organizations run on, and take action in the systems where the work actually lives. This role builds and delivers those agents in the field. You will own engagements end to end. You sit with a client's operations, clinical, and IT teams to learn how the work gets done today, then design the agent that does it, build it, integrate it into their systems, and stay with it through the first months in production. This is not a demo that works in a conference room. It runs against real patients under HIPAA. The measure of this work is whether it runs without you. An agent that performs in a pilot is a good result; one that holds up after you have moved on to the next client is the product. You may often be the only engineer in the room, and we expect you to make the call there rather than take it back to someone else. What You'll Own The whole agent. Discovery through production and through the first months of running it. The design, the integrations, the prompts and tools, and whatever happens when a call goes wrong at two in the morning. The client's business logic. Every client runs their operation differently. You learn how their flow works, and you're the one who turns that into the system. A few weeks into an engagement you should know their process well enough to catch the thing they forgot to tell you. Real-time voice. Telephony, streaming audio, speech in and speech out, latency, and sensible behavior when something fails in the middle of a live call. Unstructured input. Getting reliable structured data out of the documents a healthcare organization actually runs on, none of which arrive clean. The write path. Taking action inside client systems of record (CRM, EHR/EMR, practice management, claims, specialty pharmacy), including the cases where an action does not cleanly succeed. Quality after launch. How the system gets tested, what gets measured, and how anyone finds out when it stops behaving. You set that, not the client. Building blocks for the field. The patterns and components that make the next deployment faster than the last, plus what you learn in the field going back to Product and Platform Engineering.