Applied AI Engineer - Wellness

Vi•Austin, TX

About The Position

We’re looking for a senior engineer who can take Vi’s AI agent capabilities and deploy them across the wellness industry — fitness chains, wellness brands, and consumer health companies. You’ll build and ship intelligent voice and messaging agents that help these businesses retain members, convert leads, and grow revenue — owning everything from data ingestion and CRM integration to outbound campaign infrastructure and predictive modeling. This is a client-facing role. You work closely with enterprise customers to understand their member lifecycle, then you build the systems that automate engagement across it. You write TypeScript and Python daily. You’re comfortable with databases, data pipelines, AI/ML tooling, and the messy realities of integrating with fitness management and CRM platforms. You ship fast, you think in products, and you don’t wait for specs.

Requirements

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 or near-real-time systems: event-driven architectures, high-throughput API services, or streaming pipelines.
Production integration work with CRM platforms (Salesforce, HubSpot, or similar), marketing automation systems, 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 primary) containers, CI/CD, monitoring, and basic security practices.
Strong client-facing communication: you can run a technical working session with a customer’s operations or 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 Haves

Voice or telephony infrastructure experience — building or operating real-time call or SMS systems at scale.
LLM orchestration and agentic system design: prompt engineering, function calling, structured output, guardrails.
Experience with predictive modeling or applied ML — churn prediction, propensity scoring, segmentation, or recommendation systems.
Fitness, wellness, or consumer health industry experience: member management platforms, engagement lifecycle, retention 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 consumer health, fitness technology, or AI-native startups building customer-facing systems.

Responsibilities

Build and deploy AI-powered voice and messaging agents for wellness clients, end-to-end from member data ingestion through intelligent outreach to CRM write-back.
Own client-facing technical relationships: translate business workflows (member retention, lead conversion, reactivation, upsell) into agent configurations and production systems.
Integrate with client data and CRM systems fitness management platforms, marketing automation tools, proprietary member databases — 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.
Train and deploy lightweight predictive models on member behavioral data to power targeted outreach and intervention campaigns.
Design and maintain databases (relational and caching layers) that support real-time agent operations, campaign tracking, and performance analytics.
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.

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