AI Engineer - Fintech Automation & LLM Workflows

Lendable Ltd; United States; Hybrid; USD 120,000 - 210,000; Full time

Benefits offered by this job

Flexible working

Hybrid in-office schedule (3 days/week

Socials & connections

Health coverage

Retirement & savings

Employee referral programme

Office meals & snacks

Sustainable commuting

Job summary

Lendable is expanding its applied AI engineering team in the US to build scalable chatbots, document parsers and AI-driven workflows. You will work on loosely defined problems, test ideas quickly and turn strong concepts into reliable services, combining Python development with real-world product impact.

You will own end-to-end AI projects, from discovery to deployment and monitoring, using LLMs, prompts, and retrieval pipelines.

Qualifications

• Strong engineering skills and experience building production systems.
• Hands-on experience using LLMs in real workflows.
• Experience designing and improving prompts, retrieval pipelines, agent workflows and evaluations.
• Ability to structure ambiguous problems and test hypotheses with partial information.

Responsibilities

• Improve the chatbot's quality and expand the account actions it can complete autonomously.
• Expand our in-house document extraction pipelines to make parsing faster and more accurate.
• Design LLM-driven workflows that simplify case management.
• Find new opportunities to replace manual work with effective AI systems.
• Own solutions from discovery and prototyping through deployment, evaluation and monitoring.

Skills

Python

LLMs in workflows

Prompt design

Retrieval pipelines

Agent workflows

Production systems

Problem structuring

Stakeholder communication

Job description

Lendable is expanding its applied AI engineering team in the US to build scalable chatbots, document parsers and AI-driven workflows. You will work on loosely defined problems, test ideas quickly and turn strong concepts into reliable services, combining Python development with real-world product impact.

You will own end-to-end AI projects, from discovery to deployment and monitoring, using LLMs, prompts, and retrieval pipelines.