AI Automation Engineer

AI is everywhere. But most companies are still using it at the surface level. Our client is taking a different approach.

Instead of paying premium fees for off the shelf tools or relying on generic AI generated output, they are building their own intelligent systems from the ground up. Systems powered by real sources, proprietary data, multimodal inputs, and engineered reasoning. Systems that move from idea to deployment at speed.

They are now hiring an R&D AI Automation Engineer to lead that charge. This is not a content writing role. It is an engineering role focused on designing, prototyping, and productionalising agentic AI systems that will transform how over 130 brands operate. The successful candidate will sit at the intersection of AI research and robust software engineering, working closely with senior AI and security leadership to build autonomous workflows that drive measurable impact.

The core mission of this role is to architect and deploy intelligent agents capable of multi step business reasoning.

Working directly with the Head of AI and Head of IT and Security, the engineer will design and build agentic frameworks using tools such as LangGraph or CrewAI, enabling AI systems to execute complex tasks autonomously. This includes leading rapid proof of concept projects to validate new LLM capabilities, assessing operational efficiency gains, and benchmarking emerging open source and proprietary models for specific use cases.

Beyond research, this role is about execution.

The engineer will develop high level Python or Node.js scripts and APIs to orchestrate data flows between LLMs, vector databases such as Pinecone or Milvus, and internal business systems. They will build event based autonomous workflows and human in the loop systems that ensure outputs are accurate, secure, and scalable.

A key responsibility will be creating custom tooling that allows AI agents to interact directly with internal infrastructure and marketing platforms. This is hands on engineering work, designing the connective tissue between models, data, and real operational systems.

The engineer will also implement structured prompt versioning and manage small scale fine tuning pipelines to optimise model performance for domain specific tasks.

This position goes deeper than experimentation.

The successful candidate will work alongside DevOps to build and maintain LLMOps pipelines, ensuring continuous deployment, testing, and calibration of AI systems in production. They will optimise containerised environments using Docker and Kubernetes to handle AI workloads and embedding generation, with Google Cloud and Vertex experience considered a strong advantage. They will embed monitoring and observability into workflows to track model latency, cost, hallucination rates, and overall system performance. They will even contribute to AI powered diagnostic tooling to identify and predict integration issues across complex systems.

Crucially, this role is about translating R&D into hardened, production ready AI solutions aligned with security and DevOps standards. The engineer will act as a technical advocate for AI best practices and help foster a culture of experimentation through workshops and internal initiatives.

The ideal candidate brings at least three years of software development experience with a strong focus on AI driven automation frameworks. They are highly proficient in Python, experienced with RESTful APIs, GitOps, containerisation, and familiar with Google AI services such as Gemini and related tooling. Exposure to RAG architectures, vector databases, and orchestration frameworks like LangChain or LlamaIndex is highly desirable.

Most importantly, they combine researcher curiosity with engineering discipline. They explore new ideas, but they ship stable systems.

This is initially a fixed term contract through to the end of the year, with strong potential to extend based on impact and performance. The client is open to European based candidates and may consider either fixed term or day rate structures depending on experience.

If you are already building with Vertex, Gemini, vector databases, and agentic frameworks, and you want to move fast from ideation to market while engineering systems that genuinely replace and enhance traditional workflows, this is a rare opportunity to shape AI capability at scale. Apply now to discuss the opportunity in confidence.