AI Engineer Data & Reporting

Job description

Role

AI Engineer – Data & Reporting Job Type: Permanent / Fixed Term Location: London, UK Mode of Working: Hybrid Working Hours: 40 hours per week (Full-time) The Role As an AI Engineer – Data & Reporting , you will act as a bridge between business stakeholders and technical data teams, with a strong focus on hands-on data science, machine learning engineering, and AI solution development. Working within the Reporting team as part of the WW Sales Enablement and Transformation function, you will design, build, and deploy scalable AI solutions that generate actionable insights and power intelligent sales processes.

This role combines deep technical expertise with business understanding, requiring end-to-end ownership of AI and machine learning solutions from problem definition through deployment, monitoring, and impact measurement.

Key Responsibilities

Design and develop production-grade AI and machine learning solutions across the sales data platform. Build recommendation engines, agentic AI frameworks, Retrieval-Augmented Generation (RAG) solutions, and intelligent summarisation capabilities. Deploy machine learning models into batch and real-time environments, ensuring scalability, reliability, and performance. Engineer solutions using large language models (LLMs) and foundation models to support chatbots, semantic search, summarisation, and reporting use cases.

Design APIs, SDKs, and microservices that integrate AI, RAG, and machine learning capabilities into enterprise applications. Own the end-to-end AI/ML lifecycle, including problem definition, data exploration, model development, validation, deployment, and monitoring. Develop forecasting models, segmentation strategies, optimisation algorithms, and early warning systems to support sales performance and decision-making. Collaborate with engineering, sales operations, and business stakeholders to embed intelligent capabilities into reporting and analytics platforms.

Lead technical decision-making related to AI infrastructure, governance, model monitoring, and safety controls. Conduct experimentation, statistical analysis, and causal inference studies to evaluate business outcomes and solution effectiveness. Mentor team members and contribute to best practices in model governance, reproducibility, and data quality. Stay current with emerging AI and machine learning technologies and identify opportunities for innovation.

Your Profile

Essential Skills & Experience

Proven experience in MLOps, data engineering, software engineering, or machine learning development with recent exposure to Generative AI and advanced analytics. Strong understanding of software engineering best practices, including version control, CI/CD, containerisation, monitoring, and deployment automation. Proficiency in Python and/or R, with experience using machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn.

Experience with modern AI frameworks such as FastAPI, LangChain, LlamaIndex, or similar technologies. Hands-on experience with LLM APIs, foundation models, embeddings, vector databases, RAG architectures, and agentic AI systems. Experience working with large-scale datasets using SQL, distributed processing platforms such as Spark, and cloud-based infrastructure. Strong communication and data visualisation skills, with the ability to explain complex technical concepts to technical and non-technical audiences.

Ability to translate business challenges into scalable AI and machine learning solutions. Experience collaborating with cross-functional teams including data engineers, software developers, business stakeholders, and UX teams. Bachelor's degree in Computer Science, Engineering, Statistics, Data Science, or equivalent practical experience. </p