Senior AI Engineer (AI Engineering)

Lendable is a UK-based fintech company with over 700 employees, profitable since 2017, and backed by investors including Balderton Capital and Goldman Sachs. The company has rebuilt major consumer finance products such as loans, credit cards, and car finance, aiming to deliver money to customers quickly. Lendable is expanding into the UK and US markets, targeting large financial institutions with outdated systems.

Essential:

• 4+ years of software engineering experience
• Proven experience building AI tooling used commercially
• Strong full-stack skills in Python or TypeScript
• Frontend skills with Next.js or React
• Knowledge of MCP (Model Context Protocol)
• Experience shipping containerized software to Kubernetes
• Comfortable working with LLMs, embeddings, and AI application patterns
• Experience designing and building API integrations
• Self-starter with end-to-end ownership from problem understanding to monitoring and iteration
• Motivated by impact and seeing work used effectively

Nice to have:

• Experience with AWS Bedrock or other LLM provider APIs
• Experience with monitoring tools such as Datadog
• Experience creating guardrails for products including SLOs and DORA metrics

Build AI on top of our cloud closing agent pronto:

• Create connectors and integrations to make company data available to AI systems (Google Workspace, Slack, Jira, GitHub, Snowflake, Confluence, and more)
• Build and maintain knowledge base pipelines, MCP integrations, and API connections powering AI tooling across the business
• Work with security and data governance requirements to ensure integrations are safe and appropriate

Enable others to build with AI:

• Support internal teams to create AI-powered data sources, automated workflows, and internal tools using rapid app builder tools
• Build templates, guardrails, and building blocks to enable safe experimentation by non-engineers
• Contribute to the internal automation platform using tools like AWS Bedrock, n8n, and custom-built solutions

Deliver measurable impact:

• Collaborate with product manager and engineering lead to identify high-leverage opportunities
• Ship quickly, measure outcomes (time saved, errors reduced, adoption), and iterate based on learnings
• Stay curious about emerging tools and techniques and apply them to improve engineering output