AI Engineer

Xcede; Greater London; Hybrid; GBP 90,000 - 120,000; Full time

Job summary

Xcede partners with a global leader in applied AI to design and deliver enterprise-grade AI systems. This hybrid London role involves hands-on development, architecture, and deployment of real-world AI apps built on LLMs, with close client collaboration and delivery leadership.

The role blends software engineering and applied AI, emphasizing ownership, experimentation, and high-quality engineering in a fast-moving, product-minded culture. Occasional travel may be required.

Qualifications

• Strong Python and software engineering foundations.
• Experience delivering production AI/ML systems in enterprise environments.
• Hands-on with Large Language Models and modern AI app architectures.
• Strong backend engineering, APIs, microservices, and cloud-native development.
• Experience with LangChain, LangGraph, vector databases, Docker, Kubernetes, cloud providers.
• Ability to design scalable, maintainable systems with ops awareness.
• Strong client-facing communication skills.
• Experience across full AI delivery lifecycle is desirable.

Responsibilities

• Design and build production-grade AI applications using LLMs and modern frameworks.
• Develop scalable backend systems, APIs, orchestration layers, and microservices for enterprise AI.
• Cover full AI lifecycle from architecture to deployment, monitoring, and adoption.
• Build agentic workflows, RAG systems, multimodal AI applications, and automation tools.
• Collaborate with enterprise stakeholders to translate business problems into technical solutions.
• Mentor engineers and help raise internal engineering standards.
• Coordinate with cross-functional teams across engineering, product, delivery, and client environments.

Skills

Python

AI/ML systems

LLMs

Backend engineering

APIs & microservices

Docker

Kubernetes

LangChain

LangGraph

Vector databases

Distributed systems

Cloud platforms

Client communication

Tools

Docker

Kubernetes

LangChain

LangGraph

Vector databases

AWS

Azure

GCP

Job description

London (Hybrid, typically 1 day per week, but this will occasionally vary slightly)

Opportunities at this level in AI are exceptionally rare. Join one of the true global leaders in the space.

We’re lucky enough to be partnering with one of the world's leaders in Applied AI. They're building the most important solutions at the forefront of commercial Generative AI deployment and answering the global demand for useful, tangible AI products.

The business designs and delivers production-grade AI systems for large, market-leading clients across various industries, including financial services, retail, healthcare, travel, gaming, and critical infrastructure. Their teams work directly with globally recognised brands to build scalable AI applications that solve real operational problems, not proof-of-concept demos.

This is a highly technical, engineering-led environment focused on shipping real-world AI systems into production. The culture is fast-moving, collaborative, and deeply product-minded, with strong emphasis on ownership, experimentation, and engineering quality.

The company is entering a major phase of international growth and investment, with significant backing, ambitious hiring plans, and access to some of the most advanced AI capabilities currently available in the market.

The Role

As an AI Engineer, you’ll work within small, high-performing delivery teams designing, building, and deploying enterprise-grade AI applications powered by Large Language Models and modern AI tooling.

You’ll operate across the full delivery lifecycle from solution architecture and orchestration through to deployment, optimisation, monitoring, and client adoption. Projects are highly hands-on and often involve agentic systems, retrieval architectures, multimodal workflows, and real-time AI applications deployed into complex enterprise environments.

This role combines strong software engineering with applied AI delivery. You’ll be expected to contribute technically, communicate directly with clients, and help shape engineering best practices internally.

Key Responsibilities

• Design and build production-grade AI applications using LLMs and modern AI frameworks
• Develop scalable backend systems, APIs, orchestration layers, and microservices to support enterprise AI deployments.
• Work across the full AI lifecycle including architecture, deployment, monitoring, evaluation, optimisation, and maintenance
• Build and deploy agentic workflows, RAG systems, multimodal applications, and AI-powered automation tools
• Collaborate directly with enterprise stakeholders to understand business problems and translate them into technical solutions
• Contribute to technical leadership across projects, including mentoring engineers and improving internal engineering standards
• Work closely with cross-functional teams across engineering, product, delivery, and client environments

What We’re Looking For

• Strong software engineering foundations, particularly in Python
• Experience building and deploying production AI/ML systems in enterprise environments
• Hands-on experience with Large Language Models and modern AI application architectures
• Strong understanding of backend engineering, APIs, microservices, distributed systems, and cloud-native development
• Experience with technologies/frameworks such as LangChain, LangGraph, vector databases, Docker, Kubernetes, Azure, AWS, or GCP
• Ability to design scalable, maintainable systems with strong engineering and operational awareness
• Strong communication skills and confidence operating in client-facing environments
• Experience across the full lifecycle of AI delivery from ideation through to production deployment is highly desirable
• Agentic AI systems and orchestration frameworks
• RAG architectures and evaluation frameworks
• Real-time or voice-enabled AI systems
• Production monitoring, guardrails, latency optimisation, and cost optimisation
• Previous experience in consulting or highly collaborative delivery-focused environments