ROLE SPECIFICATIONResponsibilities & Craft Standards
What you will own day-to-day and how we evaluate impact.
Key Responsibilities & Impact
- Design retrieval pipelines, evaluation sets, and tool-using agents for client and product use cases
- Integrate LLMs safely with documents, structured data, and human approval steps
- Partner with engineering to ship AI into real products (not demos only)
- Document failure modes, costs, and monitoring for production systems
Required Craft & Core Standards
- Hands-on experience with LLM APIs and at least one RAG stack
- Python and/or TypeScript for service code
- Understanding of chunking, embeddings, evals, and prompt failure modes
- Bias toward grounding answers in customer-approved sources
Preferred Multipliers & Nice-to-Haves
- LangGraph / agent orchestration experience
- AWS, Azure, or GCP deployment for AI services
- Prior work on WhatsApp or document automation
APPLICATION PROTOCOLApply for AI Engineer (RAG & Agents)
Direct review by the founding engineering team. Zero automated keyword filtering.
