AI Applied Engineer

Summary

Join a fast-growing fintech startup utilizing AI-native tools to help people make smarter financial decisions. As an AI engineer, you will integrate LLMs into production systems, owning core features from concept to launch. You will build LLM-powered features, select appropriate models, optimize prompts, and connect models to real-world data. Collaboration with engineering and product teams is key to quickly shipping experiments and iterating based on user feedback. Staying current on foundation model trends is also essential. The ideal candidate has shipped LLM features in production and understands deployment at scale.

Requirements

• Has shipped LLM features in production and understands the nuances of deploying them at scale
• Familiar with OpenAI, Anthropic, Mistral, and similar APIs
• Experienced in vector databases and RAG frameworks (e.g., Pinecone, FAISS)
• Comfortable writing glue code in Python

Responsibilities

• Build LLM-powered features such as chatbots, summarization tools, and smart autofill
• Select appropriate models (e.g., GPT-4, Claude, open-source) based on accuracy, latency, and cost
• Optimize prompts, manage token usage, and apply techniques like Retrieval-Augmented Generation (RAG)
• Connect models to real-world data via APIs, databases, and user context
• Collaborate across engineering and product to ship experiments quickly and iterate based on user feedback
• Stay on top of the latest trends in foundation models, developer tooling, and AI infrastructure

Preferred Qualifications

• Hands-on with LangChain, LlamaIndex, or custom AI agents
• Thinks in tradeoffs and knows when to use closed vs. open models
• Strong product thinking and a bias toward action

Benefits

• Competitive compensation and meaningful equity
• Comprehensive health, dental, and vision benefits
• Remote-first team culture with high ownership and autonomy