Sr. AI Engineer

Narvar
13hRemote

About The Position

We’re building Navi — Narvar’s agentic AI that automates post-purchase resolution for the world’s leading retailers. Hundreds of millions of consumers interact with Narvar every year. Navi is our agentic AI that resolves delivery issues, returns, and refunds through natural conversation — powered by IRIS and 74 billion consumer touchpoints. We're looking for senior AI engineers to own this system end-to-end: architecture, model selection, production operations. You'll help decide what gets built and how.

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

Nice To Haves

  • You’ve worked in startup or high-ownership environments
  • You’ve built and operated AI systems in production, including monitoring and incident response
  • You’ve evaluated and iterated on LLM systems for accuracy, hallucination, latency, and cost
  • You’ve built or integrated MCP servers or similar tool-use infrastructure
  • You’ve influenced technical direction by earning trust, not by mandate
  • You use modern tooling (including AI-assisted development workflows) to increase leverage, not outsource thinking

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

Benefits

  • We are an equal-opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
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