AI Engineer (up to £130k)

Dex; Greater London; Vor Ort; GBP 110.000 - 180.000; Vollzeit

Zusammenfassung

Dex’s partner company is engineering AI agents that automate critical customer lifecycle workflows for major financial institutions, operating production-grade agentic systems. You will own the infrastructure ensuring production agents remain reliable, including evals, feedback loops, prompt engineering, and fallbacks, while shaping human-in-the-loop logic for high-stakes actions.

The role blends AI engineering with pragmatic ML across the full stack, contributing to the data strategy and

Qualifikationen

• Production experience building and maintaining agentic or LLM infrastructure: evals, feedback loops, prompt engineering, fallbacks, and human-in-the-loop decision logic.
• A pragmatic ML foundation across supervised and self-supervised methods.
• Product-leaning and full-stack in mindset; comfortable contributing to a Node and TypeScript software stack.
• Strong system design capabilities, with proven adaptability in a technical assessment (e.g., TypeScript).
• Thrives in a fast-paced startup environment, executing at pace and owning your lane while data strategy evolves.

Aufgaben

• Design and implement agent reliability infrastructure, including evals, feedback loops, prompt engineering, and fallbacks.
• Build and maintain human-in-the-loop decision logic for high-stakes agent actions.
• Take ownership of proprietary supervised and self-supervised ML models, applying recent academic work pragmatically.
• Contribute across the full agentic and software stack, including Node and TypeScript.
• Shape the evolving data strategy for production AI agents in a rapidly growing environment.

Kenntnisse

Agentic infrastructure

LLM infrastructure

Node.js

TypeScript

System design

Tools

Node.js

TypeScript

Jobbeschreibung

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 company builds and licenses AI agents that automate critical customer lifecycle workflows for major financial institutions. They run real agentic systems in production, tackling long, high-stakes banking processes. Backed by leading VCs, they're scaling rapidly with significant traction.

This is not a siloed research role. You will own the infrastructure that keeps these production agents reliable: the evals, feedback loops, prompt engineering, and fallbacks. You'll also design the human-in-the-loop logic, deciding when an agent acts and when a person steps in. It's a product-leaning seat, blending AI engineering with pragmatic ML, building across the full agentic and software stack.

The work

• Design and implement agent reliability infrastructure, including evals, feedback loops, prompt engineering, and fallbacks.
• Build and maintain human-in-the-loop decision logic for high-stakes agent actions.
• Take ownership of proprietary supervised and self-supervised ML models, applying recent academic work pragmatically.
• Contribute across the full agentic and software stack, including Node and TypeScript.
• Shape the evolving data strategy for production AI agents in a rapidly growing environment.

What You Bring

• Production experience building and maintaining agentic or LLM infrastructure: evals, feedback loops, prompt engineering, fallbacks, and human-in-the-loop decision logic.
• A pragmatic ML foundation across supervised and self-supervised methods.
• Product-leaning and full-stack in mindset; comfortable contributing to a Node and TypeScript software stack.
• Strong system design capabilities, with proven adaptability in a technical assessment (e.g., TypeScript).
• Thrives in a fast-paced startup environment, executing at pace and owning your lane while data strategy evolves.

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