Sr. Applied AI Engineer - Wellness

We’re looking for a senior engineer who can take Vi’s AI agent capabilities and make them work in production. You’ll build and ship intelligent AI agents for Wellness clients — owning everything from data ingestion and CRM integration to real-time agent infrastructure. 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 agents for Wellness 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 and build the ingestion pipelines to support them.
• Write the production stack - agent runtime, routing, orchestration, evals; help build out the ML side in Python (training, embeddings, model registry); compose declarative guardrails & integrations within workflows.
• Design and maintain databases (relational and caching layers) that support both real-time agent operations and compliance audit trails.
• 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

• 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 wide range of 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) 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.
• 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.