Job Purpose
We have a new opportunity for an AI Lead Developer at Obviously Group Limited, a subsidiary of RWS Holdings. Obviously provides brand protection and intellectual property management, and AI runs through every part of our product suite: computer vision, data science, deep learning, NLP, and AI agents in production. You'll be accountable for our AI systems end to end, lead the AI team, and shape how that team designs, evaluates, ships, and runs AI. You'll stay hands-on, building and designing AI solutions day to day, while setting the direction and standards others work to.
This role would suit a senior engineer ready to step into leadership, with a track record of building production-grade applications across a range of AI/ML disciplines such as computer vision, NLP and agentic systems. This is a hybrid role based at either our Norwich or London office.
RWS is a content solutions company, powered by technology and human expertise. We grow the value of ideas, data and content by making sure organizations are understood. Everywhere. Our proprietary technology, 45+ AI patents and human experts help organizations bring ideas to market faster, build deeper relationships across borders and cultures, and enter new markets with confidence – growing their business and connecting them to a world of opportunities. It's why over 80 of the world's top 100 brands trust RWS to drive innovation, inform decisions and shape brand experiences. With 60+ global locations, across five continents, our teams work with businesses across almost all industries. Innovating since 1958, RWS is headquartered in the UK and publicly listed on AIM, the London Stock Exchange regulated market (RWS.L).
Responsibilities
System ownership
•Own the AI estate end-to-end, across vision models, ML pipelines, LLM services, and AI agents. You are accountable for how it's designed, how it performs in production, and how it evolves.
•Set and maintain the technical direction and architecture for AI.
•Decide when to build, fine-tune, use a foundation model, or call a provider, and document those decisions so they're understood and applied consistently.
•Own production quality, cost, and reliability for AI: monitor drift, accuracy, latency, and spend, and act on what the data shows.
•Identify and drive down technical debt across the AI estate. Turn one-off scripts, notebooks and proofs of concept into shared, maintainable systems.
Leading the team and shaping how it works
•Lead the AI team day to day: set priorities, unblock work and make sure the team delivers what it commits to.
•Define how the team works, from problem framing and data collection through labelling, evaluation, release and monitoring, then write it down and make it the default.
•Make sure every model and AI feature in production is versioned, observable, and traceable, so we can always say what produced a result, when, and how well it's performing.
•Establish shared tooling, patterns and standards for the team: dataset and model versioning, evaluation harnesses, code quality and reproducibility.
•Mentor and develop engineers and data scientists through code review, pairing and knowledge sharing, and help each person grow.
•Build onboarding material, documentation, and reusable examples so new joiners are productive quickly.
Hands-on design and delivery
•Design and build AI solutions yourself, including computer-vision models, LLM-powered features, retrieval-augmented generation and agentic workflows, and lead the team's most technically demanding work.
•Own our approach to evaluating AI and vision systems: benchmark sets, metrics, regression testing between versions, and human-in-the-loop review.
•Lead our approach to automatic and model-assisted labelling of images, using foundation and zero-shot models to speed up dataset creation without losing quality.
Commercial and stakeholder leadership
•Be the point of contact for AI across product, engineering and client-facing teams.
•Turn client problems into AI work with clear success measures.
•Understand the commercial impact of AI decisions, including provider and compute cost, accuracy trade-offs and the review workload they create, and make the case for investment where it's needed.
•Give realistic estimates for research and delivery, flag risks early, and know when to stop a proof of concept.
•Explain AI capabilities, limitations, and recommendations clearly to technical and non-technical audiences, including clients and senior leadership.
•Judge which AI/ML approach best fits each situation, and pull together the resources we already have to build effective solutions, for example, combining computer vision and NLP into vision-language model (VLM) solutions for client demo labelling.
Requirements
•Commercial experience as an AI, ML or computer-vision engineer, including building, or helping to build, AI systems that are running in production.
•A track record of technical ownership: making architectural calls on AI systems.
•Experience leading, mentoring or technically guiding other engineers, formally or informally, and a clear view of how a strong AI team should work.
•Experience establishing or improving team practice, such as evaluation standards, release processes, shared tooling or ways of working.
•Strong Python, with deep working knowledge of PyTorch and the Hugging Face ecosystem.
•Experience with automatic or model-assisted labelling of visual data, and with dataset quality control.
•A solid grasp of MLOps: experiment tracking, model and dataset versioning, reproducible training and containerised deployment.
•Strong communication skills, including explaining estimates, risks, trade-offs and model limitations.
•Experience building with LLM APIs, including prompt engineering, structured output, RAG and provider trade-offs.
•Practical computer-vision experience: detection, classification, and image similarity, including fine-tuning on custom datasets.
•Experience with zero-shot and vision-language models (e.g., OWLv2, Florence-2, CLIP-style embeddings).
•Experience building AI agents, tool use, or computer-use and browser automation.
•Experience with ClearML, Label Studio, PyTorch Lightning, or LangChain.
•Experience running GPU inference on Kubernetes, AWS (including Bedrock), or GCP.
•Hands-on experience designing evaluations for vision and/or LLM systems, including baselines, metrics, test sets, and comparisons between versions.
Conditions
•Hybrid role based at either the Norwich or London office.
