Principal Applied AI Engineer

Description We’re looking for a lead engineer who can take Vi’s AI agent capabilities and make them work in production healthcare environments. You’ll build and ship AI systems for clients — owning everything from data ingestion and CRM integration to real-time agent infrastructure and HIPAA-compliant delivery. This is a client-facing role: you work closely with enterprise customers to understand their workflows, then you build AI orchestrated workflows that automate them. You’re comfortable with databases, data pipelines, AI/ML tooling, agent configuration, and the messy realities of integrating with healthcare data systems. You ship fast, you think in products, and you don’t wait for specs. Key Responsibilities Build and deploy AI-powered voice and messaging agents for healthcare and life sciences clients, end-to-end. Translate client workflows into real agent workflow design. Run technical sessions with clients' clinical, IT, and ops teams; converting needs into prompts, tools, retrieval shapes, and vector stores. Integrate with client data systems including CRMs, EHR/EMR platforms, specialty pharmacy systems, claims and Rx data feeds and build the ingestion pipelines to support them. Write the production stack in TypeScript - agent runtime, routing, orchestration, evals; help build out the ML side in Python (training, embeddings, model registry); compose declarative guardrails & integrations within workflows. Engineer within HIPAA constraints: real-time API access to PHI (never stored at rest), de-identification pipelines, US-only data residency, encrypted recordings. Design and maintain databases (relational and caching layers) that support both real-time agent operations and compliance audit trails. Implement output guardrails ensuring agents remain informational and compliant with healthcare regulatory requirements. Codify per-client configuration patterns into reusable components so each new client onboards faster than the last. Collaborate with product, account management, and platform engineering to translate field learnings into platform improvements. What We’re Looking For 7+ years in a production engineering role shipping customer-facing software. Solutions engineering or consulting backgrounds qualify if you were writing and deploying production code, not just scoping it. Fluency in modern programming languages (Javascript/Node, Typescript, Python, etc) Experience building and operating real-time systems: WebSockets, streaming media, event-driven architectures, or high-throughput API services. Production integration work with CRM platforms (Salesforce, HubSpot, or similar), healthcare data systems (EHR/EMR, claims, pharmacy), or data warehouse/lake connectors (Snowflake, Databricks, S3). Have built retrieval and ingestion paths that feed agents reliable context with correct guardrails & caching in place. Working knowledge of data engineering patterns: ETL/ELT pipelines, data quality checks, ingestion from heterogeneous sources. Comfort with cloud infrastructure (AWS, GCP): containers, CI/CD, monitoring, and basic security practices. Ability to work within HIPAA-regulated environments. You don’t need to be a compliance expert, but you understand why certain data can’t be logged and how to build systems that enforce it. Strong client-facing communication: you can run a technical working session with a customer’s IT team and translate what you learn into engineering decisions. Startup disposition: you build, you ship, you fix what breaks. Low ego, high agency. Nice to Have Voice or telephony infrastructure experience: building or operating real-time call systems at scale. LLM orchestration and agentic system design: prompt engineering, function calling, structured output, guardrails. Healthcare or life sciences domain knowledge: patient services, specialty pharmacy, clinical operations, health plan operations. Experience with workflow engines, rules engines, or state machine architectures. Product sensibility: you think about user experience, not just system architecture. You’ve influenced product direction through technical insight. Prior work at healthcare technology companies or AI-native startups building customer-facing agent systems.