Artificial Intelligence Engineer
🚀 Applied AI Engineer – Agentic Systems 🚀
✨ Series A Fintech backed by elite, repeat founders ✨
💎 Equity with real upside — not just paper value 💎
đź“Ť London (Hybrid)
💸 £100k – £200k base (depending on level)
đź’˛ ~$160k equity over 4 years
đź’— Perks: onsite chef, barber, breakfast, gym membership
This is an opportunity to join a fast-scaling, AI-native fintech that has just secured a major Series A and is now assembling a world-class AI engineering team.
Let’s be blunt — the AI market is crowded with noise right now. This isn’t one of those roles. This is a business building real systems, with real users, solving real enterprise problems.
They are developing production-grade agentic AI platforms designed for complex, high-value workflows in enterprise environments.
You’ll work directly with founders, senior leadership, and a highly selective group of engineers. No bureaucracy, no endless approvals, just high ownership, rapid iteration, and a genuine expectation to ship.
This opportunity offers accelerated learning, early ownership, and exposure most engineers don’t get until much later in their careers.
Top engineers are drawn to environments like this for a reason:
âś…High talent density
âś…Direct Founder Access
âś…The kind of pace where 12 months here can equal 3+ years elsewhere.
What you’ll actually be doing
You’ll take ownership of core parts of the agentic AI stack end-to-end, including:
- Designing and deploying multi-agent systems
- Building and scaling RAG-based architectures
- Developing evaluation frameworks focused on performance, reliability, and safety
- Delivering AI-powered features used by enterprise clients in production
You’ll also be hands-on with backend engineering (Python, FastAPI/Django), infrastructure, and deployment, making sure systems are robust, scalable, and production-ready.
With fresh funding secured and aggressive growth plans, this is a genuine inflection point and a chance to fast-track your career in a meaningful way.
Experience Required
- Educated to at least degree level in a relevant field (Maths, Stats, ML, Engineering, etc.)
- 4+ years in AI/ML engineering, or strong software engineering background with a move into AI
- Proven experience deploying LLM or agent-based systems into production
- Solid engineering fundamentals across orchestration, memory, deployment, and monitoring
- Experience with tools/frameworks like LangGraph, ReAct, CoT — or ability to build systems from first principles
- Familiarity with PyTorch/TensorFlow, RAG pipelines, vector DBs, and modern infra
- Background in startups (builders) or consultancies (client-facing delivery)
- Self-directed, adaptable, and comfortable operating in ambiguity
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