What are the responsibilities and job description for the Applied AI Engineer - Life Sciences & Healthcare position at Vi?
Role Summary
We’re looking for a senior engineer who can take Vi’s AI agent capabilities and make them work in production healthcare environments. You’ll build and ship intelligent voice and messaging agents for healthcare and life sciences 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 the systems that automate them.
You write TypeScript and Python daily. You’re comfortable with databases, data pipelines, AI/ML tooling, 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
Must Have
We’re looking for a senior engineer who can take Vi’s AI agent capabilities and make them work in production healthcare environments. You’ll build and ship intelligent voice and messaging agents for healthcare and life sciences 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 the systems that automate them.
You write TypeScript and Python daily. You’re comfortable with databases, data pipelines, AI/ML tooling, 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.
- Own client-facing technical relationships: translate business workflows into agent configurations, integration specs, and production systems.
- 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 production backend services (TypeScript) and routing/ML logic (Python). Build dashboards and internal tooling as needed.
- 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.
Must Have
- 5 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 TypeScript and Python. You’ll work in both daily.
- 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).
- Solid database fundamentals: relational schema design, query optimization, caching strategies. You’ve built systems that store and serve data at production scale.
- 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.
- 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.