Applied AI Engineer (up to £180k)

This role is with one of Dex's trusted partner companies. We work closely with their teams to truly understand their culture, goals, and what they're looking for, so we can match you with the right opportunity and give you context about the role before you commit to a process.

Dex is an AI recruiter agent that helps you run your job search. Tell Dex your stack, seniority, and what you want to build. We will manage your applications and surface other opportunities that are a fit.

The role

This platform automates document processing and data retrieval for enterprise customers, operating in highly sensitive financial environments where compliance isn't optional. Backed by leading investors, they've recently secured significant Series A funding, positioning them as one of the fastest-growing enterprise AI platforms globally.

You’ll join a lean team, reporting directly to the founders, to drive the AI-product surface. This is an applied seat: you’ll ship production-grade AI features within a regulated enterprise platform, not just prototype in a notebook. You’ll own the development and deployment of AI-powered features, from RAG systems to multi-agent infrastructure, ensuring they meet the highest standards of scalability, performance, and security.

The work

• Develop and deploy production‑grade AI features, including RAG systems, multi‑agent infrastructure, and robust evaluation frameworks.
• Build the backend services and APIs that power these AI applications.
• Design and implement data pipelines for training, evaluation, and continuous improvement of AI models.
• Ensure the scalability, performance, and security of AI systems across the entire stack.
• Mentor junior engineers and contribute to team growth.

What You Bring

• 5+ years of professional software engineering experience, with a track record of shipping production systems.
• Proven experience building and deploying AI applications in production, specifically with RAG pipelines, agent orchestration, and performance monitoring.
• Fluency in LLM evaluation techniques for accuracy, reliability, and safety.
• Strong backend development skills in Python (Django/FastAPI preferred) and relational databases.
• Experience with cloud infrastructure, Docker, Kubernetes, CI/CD, and managing high-throughput background workers and queues.