AI Engineer

The role We are looking to hire an AI Engineer to join our Data team in London. This is an office-based role out of our London office. You must be based in the UK and have existing, full rights to work to be considered for this role. Working at WGSN Together, we create tomorrow  A career with WGSN is fast-paced, exciting and full of opportunities to grow and develop. We're a team of consumer and design trend forecasters, content creators, designers, data analysts, advisory consultants and much more, united by a common goal: to create tomorrow.  WGSN's trusted consumer and design forecasts power outstanding product design, enabling our customers to create a better future. Our services cover consumer insights, beauty, consumer tech, fashion, interiors, lifestyle, food and drink forecasting, data analytics and expert advisory. If you are an expert in your field, we want to hear from you.    Role overview  The foundation of WGSN is our passionate experts. WGSN seeks talent globally to work within a business that offers a unique blend of specialist problem solvers, engineers, data scientists, and innovative thinkers who put trends, creativity and data together to create tomorrow. As a Full-Stack AI Engineer, you will be responsible for taking AI/LLM models from prototype to production-grade systems that power WGSN products and internal tools. You will be a core member of the Data Science and Engineering function, working closely with data scientists, machine learning engineers, data engineers, and product teams to operationalise models reliably and at scale. This role requires strong engineering expertise and the ability to transform PoC outputs into robust, secure, high-performing services. You will design and build APIs, inference services, CI/CD pipelines, evaluation frameworks, vector search capabilities, and monitoring systems. This role is essential to enabling WGSN’s next-generation AI capabilities and ensuring models deliver consistent, high-quality performance in production environments. This position requires a highly pro-active, hands-on engineer who enjoys problem-solving, fast learning, and working across the full AI lifecycle. Key accountabilities AI Architecture & Model Engineering - Build, deploy and maintain production-grade LLM and multimodal models. - Convert experimental notebooks into robust, testable, production-ready services. - Design and implement retrieval-augmented generation (RAG) systems, semantic search pipelines, embeddings, and vector search infrastructure. ML/LLM Operations (LLMOps / MLOps) - Develop CI/CD pipelines for model deployment, versioning, evaluation and rollback. - Build scalable inference infrastructure using Docker, Kubernetes, and AWS services including Lambda, ECS/EKS, API Gateway, S3 and CloudWatch. - Implement and maintain monitoring for latency, throughput, drift, hallucinations, quality, reliability and operational cost. Backend Engineering - Design and develop robust APIs and microservices (Python/FastAPI preferred) to expose AI functionality into WGSN products. - Optimise inference performance through batching, caching, autoscaling and efficient resource utilisation. - Ensure all AI services meet reliability, scalability, performance and security requirements. Data Quality, Evaluation & Governance - Collaborate with Data Engineering and DataOps teams to ensure high-quality, reliable data pipelines for AI training and inference. - Build automated evaluation pipelines, test suites and guardrail systems to ensure safe, predictable model behaviour. - Contribute to AI governance, safety, compliance and responsible-AI frameworks. Cross-functional Collaboration - Work closely with data scientists, machine learning engineers, data engineers, analysts, platform engineers, designers and product managers to embed models into production features. - Communicate complex technical concepts clearly and effectively to non-technical stakeholders. Continuous Learning - Stay current on developments in LLMs, multimodal AI, optimisation techniques, AWS technologies and modern MLOps/LLMOps practices. - Evaluate emerging tools, frameworks and best practices to enhance scalability, performance, reliability and developer efficiency. This list is not exhaustive and there may be other activities you are required to deliver. Skills, experience & qualifications required Essential - Strong software engineering experience with Python, including building production APIs and services (FastAPI or similar). - Hands-on experience deploying and operating LLM-based systems in production environments. - Practical experience with RAG architectures, embeddings, vector databases, and semantic search. - Experience with AWS services (e.g. Lambda, ECS/EKS, S3, API Gateway, CloudWatch). - Solid understanding of CI/CD, containerisation (Docker) and orchestration (Kubernetes). - Experience designing scalable, reliable, and secure backend systems. - Strong problem-solving skills and ability to move from prototype to production-grade solutions. - Ability to collaborate effectively with data scientists, data engineers, product and platform teams. Desirable - Experience with multimodal models (text, image, video).Familiarity with LLMOps/MLOps tooling, evaluation frameworks, monitoring and guardrails. - Experience working in product-led or data-driven organisations. - Experience optimising inference performance and managing model cost at scale. Qualifications - Degree in Computer Science, Engineering, Mathematics, or a related field , or equivalent practical experience. - Demonstrated track record of delivering AI or ML-powered systems into production.