Negotiable
OverviewLocation: London (Hybrid - 2-3 days/week in-office)
Compensation: Competitive salary + meaningful equity
Visa sponsorship: Not available
A fast-growing AI startup is on a mission to reinvent how companies manage their finances building AI tools that finance teams can actually trust, control, and use at scale.
Why This Role Is Special
Real Ownership:
As a founding engineer, you''ll lead the design and development of production AI systems - shaping architecture, tooling, and user experience.
Built on Solid Foundations:
The platform already integrates with major accounting systems and serves thousands of users. You''ll be building on something proven - not starting from scratch.
Meaningful Impact:
Your work won''t be an internal experiment. It''ll directly affect how real businesses understand and manage their finances.
AI With Purpose:
This isn''t about flashy demos - it''s about building intelligent tools that are safe, explainable, and reliable in production.
What You''ll Be Doing
Design and Build Production AI Systems:
Architect scalable LLM-based features and infrastructure that power intelligent agents and copilots for finance teams.
Measure What Matters:
Build robust evaluation systems using curated test sets, automated checks, and CI-integrated safeguards.
Safety and Compliance First:
Implement schema validation, content filtering, authorisation controls, and circuit breakers to ensure user trust and regulatory safety.
Optimise Retrieval-Augmented Generation (RAG):
Improve document chunking, indexing, and search pipelines - with a focus on latency, precision, and cost-efficiency.
What You Bring
A deep interest in building with
AI and LLMs
in real-world environments.
A strong backend engineering foundation (4+ years), ideally with experience in
TypeScript ,
Python , or
Kotlin .
Hands-on experience shipping
LLM applications or AI agents , using tools like
LangSmith ,
Promptfoo ,
TruLens ,
DeepEval , or
Phoenix .
Solid understanding of
prompt engineering ,
function calling ,
structured outputs , and
evaluation techniques .
Experience with
RAG systems , including embeddings, vector databases, and retrieval strategies.
A strong product mindset, clear communication skills, and comfort with the pace and ambiguity of early-stage startups.
Bonus: Experience working with sensitive business or financial data, especially with attention to security, observability, and compliance.
You\''ll Thrive If You…
Are excited by autonomy, impact, and the chance to build a product from 0 to 1
Want to work closely with founders and have a voice in company strategy
Prefer thoughtful, production-grade AI over quick hacks and demos
Care about
transparency, safety, and trust
in AI ..... full job details .....
Negotiable