We're looking for an AI Engineer to join our AI Engineering team at Lendable to help us enable developers to move faster whilst keeping quality high.
Our mission is to bring all the lessons we have learned about AI development so far into one system so everyone can benefit. The system combines our Lendable Coding Agent - pronto, a system for measuring effectiveness of AI and the stability of our systems.
This is a role where you'll be working with our engineers and product managers who build our products. You will improve the developer experience and improve our throughput. You’ll see instant impact with your work and participate in a highly collaborative environment.
We need someone who takes full ownership — not just writing code, but thinking through the problem, designing the solution, shipping it, and making sure it keeps working. You'll own your work from "what should we build?" through to "is it still delivering value?".
You'll also be working at the frontier of AI tooling — building with LLMs, experimenting with new approaches, and figuring out what's possible.
What you'll be doing
Build AI on top of our cloud closing agent pronto
Create connectors and integrations that 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 that power 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 their own AI-powered data sources, automated workflows and internal tools using rapid app builder tools
Build templates, guardrails and building blocks that make it easy for non-engineers to experiment safely
Contribute to our internal automation platform using tools like AWS Bedrock, n8n and custom-built solutions
Deliver measurable impact
Work closely with the PM and engineering lead to identify the highest-leverage opportunities
Ship quickly, measure outcomes (time saved, errors reduced, adoption) and iterate based on what you learn
Stay curious about emerging tools and techniques — and apply them where they'll genuinely make a difference to our engineering output
What we're looking for
Essential
4+ years of software engineering experience
Proven experience building AI tooling used by others in a commercial environment
Strong full-stack skills in Python or TypeScript
Frontend skills with Next.js or React to build an interface for engineers
Knowledge of MCP (Model Context Protocol)
Experience shipping containerised software to Kubernetes
Comfortable working with LLMs, embeddings and AI application patterns
Experience designing and building API integrations
Self-starter who takes ownership end-to-end — from understanding the problem, through design and implementation, to monitoring and iteration
Motivated by impact — you want to see your work used and making a difference
Nice to have
Experience with AWS Bedrock or other LLM provider APIs
Experience with monitoring tools such as Datadog
Experience with creating guardrails for products: SLOs, DORA metrics