About the job
Join Our Team as a Senior AI Engineer
Be a part of Narvar's innovative journey as we develop Navi, an intelligent AI system that streamlines post-purchase resolution for top retailers worldwide.
Each year, millions of consumers engage with Narvar. Navi, our cutting-edge AI, tackles delivery issues, manages returns, and handles refunds using natural language processing, driven by IRIS and an extensive dataset of 74 billion consumer interactions.
We are seeking experienced AI engineers who are ready to take ownership of this complex system—from architecture and model selection to production operations. You will play a crucial role in shaping the direction of our projects.
Your Responsibilities
- Design and implement conversational AI agents for returns, claims, and customer service interactions.
- Oversee the entire lifecycle of agent systems from architecture to implementation, evaluation, and production operations.
- Develop retrieval pipelines for context graphs that enhance agent responses with real company and customer data.
- Design orchestration for multi-step workflows that engage with identity, risk, order, and loyalty systems.
- Create frameworks for evaluating task completion, accuracy, safety, and user satisfaction.
- Establish guardrails and safety measures, including content moderation, hallucination detection, and graceful fallbacks.
- Integrate conversational experiences across web, mobile, SMS, and email platforms.
- Make informed decisions about prompt design, model selection, and trade-offs regarding latency, cost, and quality.
- Collaborate effectively with product, design, and ML teams to develop technically sound and user-centric systems.
Qualifications We Seek
We value judgment and ownership over formal qualifications.
You may be a great fit if you:
- Have deployed conversational AI or agent-based systems that are actively used by real users.
- Have constructed production systems leveraging LLM APIs and agent frameworks, focusing on reliable integrations rather than mere prompt experimentation.
- Possess insights into model selection trade-offs, including when to utilize frontier APIs versus open-weight models (e.g., Qwen, Llama, Mistral), understanding the implications for cost, latency, privacy, and capabilities.
- Understand advanced prompt engineering techniques, including structured outputs, few-shot learning, and tool invocation.
- Have experience in building context graph pipelines that extend beyond basic retrieval methods.

