Senior AI Software Engineer, Lendable

• 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

Desirable

• We're looking for a hands-on AI Engineer 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
• 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
• 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
• 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
• You'll join a small, focused team where you'll have real ownership over what you build. Work comes as problem statements with clear direction from the engineering lead and PM — you'll figure out the "how", design the approach, build it, and make sure it keeps delivering value
• We value shipping and learning over perfection. The goal is always to deliver something useful, learn from how it's used, and improve. You won't be directly client-facing, but your work will directly impact colleagues across the business — and you'll hear about it when something you built makes their day easier
• See your work make a difference This isn't a team where your code disappears into a monolith. You'll build something on Monday and see it saving someone time by Friday. Every integration and tool you ship has a direct line to company efficiency
• High leverage A small team means your contributions have outsized impact. No layers, fast decisions, real ownership
• Build new things We're building a platform from the ground up, not maintaining legacy systems. You'll shape how AI gets used across Lendable
• Work at the frontier AI tooling is moving fast. You'll work with the latest in agentic AI, workflow orchestration and LLM tooling — applied to real problems, not just proof-of-concepts

• Screening call with Hiring Manager
• Take-home task
• Technical interview based on the task
• Final interview