About the role
We're looking for a hands-on AI Engineer contractor to join our Internal Automation team at Lendable and help us make the whole company more efficient.
Our mission is to supercharge internal teams — from Finance and Compliance to Product, QA and beyond — by building AI-powered tools, integrations and automated workflows. You'll be part of a small team (4 engineers, 1 PM) with a simple goal: remove friction, automate the tedious, and give colleagues back time to focus on high-value work.
This is a role where you'll see the direct impact of what you build. You'll ship an integration and watch it save hours of manual work. You'll build a tool and see a team adopt it the same week. If you're motivated by solving real problems and seeing your work make a tangible difference, this is for you.
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 integrations and data sources
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 move the needle
What we're looking for
Essential
4+ years of software engineering experience
Strong full-stack skills in Python or TypeScript
Experience shipping containerised software to Kubernetes
Proven experience building AI tooling used by others in a commercial environment
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 workflow automation tools (n8n, Zapier, Make or similar)
Familiarity with vector databases (Pinecone, Weaviate, pgvector)
Experience with AWS Bedrock or other LLM provider APIs
Experience of building and rolling out MCPs
Experience of building and rolling out AI Skills
Frontend skills with Next.js or React for internal tooling
Interview process
Screening call with Hiring Manager
Technical interview based on a theoretical task
Interview with Product Manager
