Applied AI Engineer – Series A start-up

• London (5 days per week, in-office)
• $160k vested over 4 years (annual vesting)
• Catered breakfasts & dinners, gym membership, private healthcare

This Is Not a Lifestyle Role. It is a career-defining one.

This role sits inside a fast-growing, AI-native company building production-grade agentic systems for complex, high-stakes enterprise workflows. The business has scaled at exceptional speed, closed a major Series A, and is now focused on building a small, elite engineering team to own the core AI infrastructure end-to-end .

The Environment

• 12 hours a day, 5 days a week, in the office
• Exceptional, high-performing teams
• Engineers work directly with founders daily
• No layers, no slow approvals, no hand-offs

The intensity is deliberate. The aim is compression:

• Faster learning curves
• Faster iteration cycles
• Faster ownership and decision-making

If you want flexibility, this isn’t the role.

If you want to become exceptional very quickly, it might be the best move of your career.

What You’ll Actually Be Building

You will own and drive large portions of the AI agent infrastructure, from design through to production deployment .

This includes:

• Designing and deploying multi-agent systems
• Building and integrating RAG pipelines
• Creating evaluation frameworks (evals) to measure accuracy, reliability, and safety
• Shipping AI-powered features used by real enterprise customers
• Building backend services and APIs (Python, Django / FastAPI preferred)
• Working across the stack — APIs, databases, infrastructure, and deployment pipelines
• Ensuring systems are scalable, performant, and secure in production

You’ll also:

• Build data pipelines for model training and continuous improvement
• Work with cloud infrastructure, containers, and CI/CD
• Collaborate directly with founders, designers, and growth teams
• Mentor junior engineers and raise the technical bar across the team

You will not be:

• Tuning prompts endlessly
• Producing throwaway PoCs
• Working on slideware or “AI demos”

Who This Is For

You’ll likely have:

• Foundational software engineering experience
• Hands-on experience designing and deploying AI systems into production, end-to-end
• Strong backend engineering skills (Python)
• Experience with relational databases, Redis, task queues, and background workers
• Familiarity with Docker, Kubernetes, and modern cloud infrastructure
• Experience with RAG, agent orchestration, and LLM evaluation techniques
• A high tolerance for ambiguity and shifting priorities

Comfort with discomfort is essential. Timelines will move. Priorities will change. That’s part of building something real .

Why Exceptional People Say Yes

Top engineers don’t optimise for comfort — they optimise for trajectory.

People choose environments like this because:

• Talent density permanently raises their bar
• Founder access is direct and unfiltered
• One year of learning feels like several elsewhere
• The experience compounds long after they leave

The Bottom Line

This role is not for everyone — and it’s not meant to be.

But if someone wants to:

• Build real agentic AI systems used at scale
• Own critical infrastructure, not just features
• Learn directly from founders who’ve built and exited before
• Trade short-term intensity for long-term career acceleration

Then this is one of the most compelling Applied AI Engineering opportunities in Europe right now.

A team of engineers is being hired rapidly.

If this reads as intimidating and exciting — that’s usually the right signal.