AI Engineer

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.

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.   

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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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. 

Our benefits and wellbeing package offers flexible benefits you can tailor to your own personal needs, including:  

- 25 days of holiday per year - with an option to buy/ sell up to 5 days

- Pension, Life Assurance and Income Protection Flexible benefits platform with options including Private Medical, Dental Insurance & Critical Illness 

- Employee assistance programme, season ticket loans and cycle to work scheme 

- Volunteering opportunities and charitable giving options  

- Great learning and development opportunities.

WGSN is the global authority on consumer trend forecasting.

We help brands around the world create the right products at the right time for tomorrow’s consumer.

The future is everything, it happens everywhere. WGSN is the world-leading forecaster because we track and analyse consumer behaviours, product innovation, design and creativity, everywhere.

We utilise our global resources and intelligence to research, source and analyse quantitative and qualitative data to produce our forecasts. Everything we do is focused on working with our customers to create a successful and positive tomorrow.

We source, review and assess quantitative and qualitative data to produce robust, actionable forecasts. To provide credible insights and design solutions for our clients, it is essential that rigour runs through everything we do.

An inclusive culture is one of our key priorities. We want our people to truly be themselves and thrive. We love having a diverse team of people who bring new ideas, different strengths and perspectives & reflect the global audience we work with.

Inclusive workforce 

We are committed to supporting the environment and sustainability, including ensuring our pension plan defaults to sustainable options and striving to be net zero by 2030.

Recognising great performance is a key part of our culture. Our Awards schemes recognise and reward the brilliant achievements of our people.  

We offer a flexible working environment with a wide range of flexible, hybrid and agile working arrangements. Conversations about flexible working have always been—and will continue to be—actively encouraged here, but we do not offer full remote working. 

We want to ensure everyone has the opportunity to perform their best when interviewing, so if you require any reasonable adjustments that would make you more comfortable during the process, please let us know so that we can do our best to support you.