Location: London

Employment Type: Full-time

Compensation : £85-95 + Equity package

Role Overview

An AI Engineer builds applications that leverage large language models (LLMs) and other foundation models, creating user-facing products and features by integrating these models into software applications. This role sits between traditional software engineering and ML engineering, typically closer to software engineering but with specialised AI knowledge.

Growing Into This Role as a Full-Stack Engineer:

If you're a full-stack engineer wondering whether you're qualified for AI engineering, the answer is likely yes. Most of AI engineering is familiar territory: integrating APIs, building user interfaces, managing databases, and handling asynchronous operations. The main difference is that instead of calling Slack or Twilio, you're calling Gemini or Claude. Your experience with unpredictable third-party APIs, performance optimisation, and building responsive user experiences translates directly. You don't need a machine learning PhD – you need solid engineering fundamentals and curiosity about new tools. The AI-specific concepts (prompt engineering, embeddings, vector databases) are learnable skills, not academic prerequisites

About Tangible

Tangible is a seed stage climate / fintech startup looking to unlock critical capital for hard tech innovators. Our platform offers tools to help these companies all a the way through their financing journey. We are a small and nimble team, passionate about seeing these hardware companies succeed. The company has 3 co-founders with a mix of product, technology and capital markets expertise. You can read more on our thesis here.

Key Responsibilities

• Design and implement AI-powered features and applications using LLMs and other foundation models
• Evaluate and select appropriate models for specific use cases
• Implement techniques like Retrieval-Augmented Generation (RAG), prompt engineering, and AI workflows or agents
• Build and manage vector databases for efficient embeddings data storage and retrieval
• Create and maintain AI tooling infrastructure
• Develop and maintain automated evaluation pipelines to measure AI feature performance
• Handle LLM-specific challenges like non-deterministic outputs, latency, and privacy
• Optimise for AI inference costs while maintaining quality