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 modelsEvaluate and select appropriate models for specific use casesImplement techniques like Retrieval-Augmented Generation (RAG), prompt engineering, and AI workflows or agentsBuild and manage vector databases for efficient embeddings data storage and retrievalCreate and maintain AI tooling infrastructureDevelop and maintain automated evaluation pipelines to measure AI feature performanceHandle LLM-specific challenges like non-deterministic outputs, latency, and privacyOptimise for AI inference costs while maintaining qualityTechnical Skills
Strong software engineering fundamentalsExperience with AI observability tools (e.g. Langfuse, OTEL) and automated evaluation pipelinesKnowledge of vector databases (e.g. Postgres with pgvector, Pinecone)Familiarity with cloud AI services (e.g. AWS Bedrock, GCP Vertex AI)Understanding of prompt and context engineering techniquesExperience with Python and JavaScript for AI application developmentExperience with frontend frameworks (e.g. React, Vue) for AI UX implementationSoft Skills
Domain understanding to assess output quality in subjective scenariosAbility to iterate rapidly with non-deterministic systemsComfort with ambiguity and the "vibes-based" nature of AI developmentCreative problem-solving to use AI capabilities effectivelyStrong communication skills to educate stakeholders on AI capabilitiesAbility to balance innovation with responsible AI useHow is this different from ML/Data roles
Focus on application integration rather than model trainingEmphasis on product features and user experienceTypically works with existing foundation models via APIs (or running open models)"Fire, ready, aim" approach: prototype first, collect specific data laterSoftware-centric workflows rather than data science pipelinesIntegration rather than mathematical/statistical expertiseSuccess Metrics
Quality and performance of AI featuresUser satisfaction with AI-powered experiencesInference cost efficiencyResponsible implementation with appropriate safeguardsInnovation in applying AI to solve real-world user problemsWhy Join Us
Founding impact: Shape our design DNA and build something from zero to oneMission-driven: Work on products that create positive climate impactGrowth opportunity: Lead design as we scale and potentially build a teamCompetitive package: Equity stake reflecting your foundational contributionDirect influence: Work closely with founders and have significant input on product directionWhat does the hiring process look like?
Screening Calls
Introductory call with CPO & Founding Engineer~ 30 minutesFollow up email, with feedback and initial questionsInterviews
Programming / Problem Solving session together with the founding product team. No preparation needed and ideally we do this in person in our London office. ~90 minFollow up email, with feedback and questions30 minute call with CEO to answer your questions on the companyOffer stage
Reference callsOffer callReady to help us build the future of climate fintech?
Send us your resume, and a brief note about why you're excited about this opportunity to: careers@tangible.finance
