About CleverChain

CleverChain is a growth-stage RegTech company, an award-winning KYB platform recognised by Chartis Research for Best Know Your Business (KYB) and by Datos Insights for Best KYC/KYB Innovation. Our cloud-based platform automates compliance processes, streamlining customer onboarding and risk monitoring for financial institutions, fintechs, payment providers, and any organisation that needs to verify and manage their customers. We're at a pivotal moment of growth and looking to make critical hires that help us get there

Role Overview

We're looking for a curious, detail-oriented Prompt Engineer to join our team on a paid internship basis. You'll play a hands-on role in shaping how large language models behave across our product, crafting system prompts, defining structured output schemas, and optimising LLM interactions within automated workflows.

This isn't a "watch and learn" internship. You'll be directly responsible for configuring and refining the AI layer of real, production-facing systems. If you spend your evenings tinkering with AI models, benchmarking them against each other, or reading papers about tool-use or even building your own AI solutions, we want to hear from you.

What You'll Be Doing

Designing and iterating on system prompts that guide LLM behaviour within multi-step automated workflows
Defining and maintaining structured output schemas (JSON Schema, function calling formats, etc.) to ensure reliable, parseable model responses
Working with search-grounded LLMs, configuring retrieval and grounding strategies to improve factual accuracy and relevance
Operating across multiple models and providers (OpenAI, Anthropic, Google, open-source models, and others), understanding their respective strengths, quirks, and trade-offs
Testing, evaluating, and optimising prompt performance, measuring output quality, latency, token efficiency, and consistency
Documenting prompt patterns and best practices to build an internal knowledge base the wider team can learn from

Skills & Experience

Must-Haves

Demonstrable experience with prompt engineering — whether professional, academic, or through serious personal projects. Show us what you've built or figured out.
Strong understanding of LLM request optimisation — token management, context window strategy, temperature/sampling tuning, and knowing when a problem is a prompt problem vs. a model problem
Hands-on experience with structured outputs — you understand how to coerce a model into returning clean, schema-compliant responses and how to handle it when it doesn't
Familiarity with multiple LLM providers and models — you know the landscape and can articulate why you'd pick one model over another for a given task
Excellent written communication — prompt engineering is, at its core, a writing discipline. Precision and clarity in natural language are non-negotiable.
A genuine passion for LLMs and agentic AI — you follow the space closely, you have opinions, and you're excited about where it's heading

Nice-to-Haves

Experience with workflow automation platforms (e.g., N8N, Make, Zapier, or similar tools)
Familiarity with agentic AI patterns — tool use, planning, multi-step reasoning, agent loops
Exposure to evaluation frameworks or techniques for assessing LLM output quality
Basic programming ability (JavaScript) — enough to read and understand code, even if you're not writing it daily

The Kind of Person Who Thrives Here

You're self-directed. You don't wait to be told what to try next — you hypothesise, test, and report back.
You're obsessively iterative. A prompt that "mostly works" isn't good enough. You refine until it's robust.
You communicate clearly. You can explain to a non-technical stakeholder why a prompt behaves the way it does and what the trade-offs are.
You're adaptable. The models change, the best practices change, the landscape shifts every few weeks. You keep up, and you enjoy it.
You think in systems, not just single interactions. You understand that a prompt exists within a broader workflow and that upstream and downstream context matters.

What We Offer

Shape the AI Layer from the Ground Up: You'll be joining early and having a direct hand in defining how LLMs behave across our product. The prompt patterns, schemas, and conventions you establish will become the foundation others build on.
Work on Interesting Problems: Search-grounded generation, multi-model orchestration, structured output reliability, agentic workflows - the challenges here are real, varied, and at the cutting edge of applied AI. This isn't busywork.
Exposure to a Production AI Stack: You'll be configuring and optimising LLM interactions in real, shipped software — not a sandbox, not a side project. What you build will be used.
Direct Impact: In a small team, your work ships and matters immediately. A prompt you refine on Tuesday could be in production by Wednesday. You'll see the results of your contributions every day.
Flexible Hours: We have core hours for alignment, but we trust you to manage your time. Get the work done, pick the schedule that works for you.
A Culture of Trust and Collaboration: Open and frequent communication, mutual respect, and a shared commitment to building something that matters. We'll treat you as a teammate, not "the intern."
Room to Grow: This is a paid internship, but we invest in people, not just positions. Promising candidates will have genuine opportunities for self-development, mentorship, and skill-building - and outstanding performance may lead to further roles within the company as we scale.

Application process

Attach your CV to the LinkedIn application and a brief note about what caught your interest. If you have a GitHub profile, side project, or writing that shows how you think about software, we'd love to see it, but it's not required.

Our process is a conversation about your experience, a practical technical discussion about real problems.

CleverChain

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