Note: The job is a remote job and is open to candidates in USA. Vi is seeking a senior engineer to bring its AI agent capabilities into production for Wellness clients. The role involves building and deploying intelligent agents, integrating CRM and healthcare data systems, developing real-time agent infrastructure, and translating enterprise customer workflows into automated AI solutions.
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
Skills
- 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
- 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
- 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
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