Applied AI Lead deploying Heidi’s healthcare AI for strategic healthcare accounts. Driving adoption, expansion, renewal, and measurable clinical and operational value.
About the role
- Senior AI Engineer responsible for developing conversational AI for post-purchase resolutions at Narvar. Designing systems for returns, claims, and customer service experiences in a remote setting.
Responsibilities
- Design and build conversational AI agents for returns, claims, and customer service experiences
- Own agent systems from architecture → implementation → evaluation → production operations
- Build RAG / context graph retrieval pipelines that ground agent responses in real company and customer data
- Design agent orchestration for multi-step workflows that interact with identity, risk, order, and loyalty systems
- Create evaluation frameworks to measure task completion, accuracy, safety, and user satisfaction
- Implement guardrails and safety mechanisms — content moderation, hallucination detection, graceful fallbacks
- Integrate conversational experiences across web, mobile, SMS, and email channels
- Make real decisions around prompt design, model selection, latency/cost/quality tradeoffs, and failure modes
- Collaborate with product, design, and ML teams to build systems that are technically sound and product-aware
Requirements
- Have shipped conversational AI or agent-based systems used by real users in production
- Have built production systems on top of LLM APIs and agent frameworks — not just prompt playgrounds, but real integrations involving tool orchestration, context management, and reliability at scale
- Have a point of view on model selection tradeoffs — when to use frontier APIs vs. open-weight models (Qwen, Llama, Mistral), and understand the cost, latency, privacy, and capability tradeoffs of each
- Understand prompt engineering beyond basics: structured outputs, few-shot learning, chain-of-thought, tool calling
- Have built context graph pipelines that go beyond naive retrieval — entity resolution, relationship modeling, and dynamic context assembly from structured and unstructured data
- Have designed agent architectures that use function calling, tool execution, or multi-step reasoning
- Have strong programming skills in Python or TypeScript
- Have experience building and integrating APIs and backend services
- Are comfortable reasoning about evaluation, safety, and reliability in non-deterministic systems
- Take initiative naturally and are comfortable operating with ambiguity.
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