Applied AI Lead

Overview AI is already making a real impact at Chambers and Partners, with solutions running across our editorial, research, product and commercial teams. We’re now looking for an Applied AI Lead to lead our AI engineering team and help shape the next stage of how we use AI across the business.

This is a hands-on leadership role for someone who enjoys solving complex technical problems while bringing people, priorities and ideas together. You'll be the most senior technical voice on AI at Chambers, responsible for defining what good looks like in AI engineering: the standards, modelling choices and evaluation discipline the team works to.

You’ll stay close to the technology while having the opportunity to influence what we build, why we build it and where AI can create the greatest value. Main Duties and Responsibilities

Lead the delivery of multiple AI initiatives, from discovery and scoping through to production and measuring their impact.

Set technical direction across AI architecture, model selection, data and retrieval strategies, evaluation and deployment.

Design scalable, secure and maintainable AI solutions in partnership with our Technology and Data teams.

Lead, mentor and develop our team of AI engineers, creating an environment where people can do their best work.

Champion high-quality engineering practices, including code review and safe, structured and streamlined deployment.

Work closely with Product, Research, Commercial, Data and Technology teams to turn business challenges into practical AI solutions and manage cross-team dependencies.

Advise senior stakeholders on technical options and trade-offs, helping prioritise the opportunities that will create the greatest value.

Keep Chambers connected to developments in AI and identify where emerging technologies can be applied in useful and responsible ways.

Why you should apply AI is already in production at Chambers - models running live across editorial, research, product and commercial workflows.?The problems are genuinely hard, largely unsolved, and the output reaches a global professional audience that depends on getting it right.

It's also a role with room in it. You'll line-manage the AI engineering team and own technical direction, but you'll stay close to the code - this isn't a job where you hand the interesting problems to someone else. You'll work directly with senior stakeholders, help decide what we build rather than just how, and because this is a focused team rather than a large one, the decisions you make show up quickly in what ships.

Chambers is midway through a significant AI transformation. If you want to shape how a category-defining company applies AI - this is the role. Skills, Experience & Personal Attributes We're looking for someone who has come up through data science, with a track record of applying data science, machine learning and GenAI workflows to solve real business problems and taking models into production, and who has gone on to lead engineers and deliver multiple technical workstreams.

You’ll bring

Essential

A data science foundation, applied for outcomes: framing the business question, setting baselines, choosing the right metric and delivering measurable results rather than just models. This comes with statistical rigour: experimental design, sound validation, and an instinct for leakage, bias and overfitting in messy real-world data.

Strong ML fundamentals across classical ML and deep learning (pandas, scikit-learn, XGBoost or LightGBM, PyTorch), and the judgement to tell when a language model is the wrong tool.

A deep understanding of how modern models are built, not only how they're used: transformer architectures, pre-training and post-training methods (SFT, RLHF/DPO) and evaluation methodology. You will have fine-tuned models yourself, for example with the Hugging Face ecosystem (Transformers, PEFT/LoRA, TRL), and can explain from first principles why a system fails.

NLP depth on long-form professional text: classification, information extraction, entity resolution, summarisation, embeddings and retrieval.

Real-world delivery of modern AI approaches (RAG, fine-tuning, agentic AI and multimodal models) in production workflows. You'll have built on model APIs (e.g. Azure OpenAI, Anthropic, open-weight models) and evaluation tooling, and know when the framework, or the LLM, is unnecessary.

Hands-on engineering skills in Python and SQL, taking work from notebook to production to a high standard (testing, CI/CD, containers, infrastructure-as-code), with production experience on Azure, ideally Azure ML and Databricks. You'll work as a peer of the platform engineering teams.

Rigour in running AI in production: experiment tracking and model registry (MLflow), evaluation frameworks and regression testing before release, monitoring and data-drift management, LLM observability and model governance.

A track record of owning technical decisions across model selection, data and retrieval strategy, evaluation and production deployment, and of contributing AI expertise to solution architecture.

Proven leadership of AI, ML or data science teams: hiring, mentoring and growing a team that ships, and moving it from prototype habits to production discipline. You'll run delivery through Scrum or Kanban, adapted for AI work with time-boxed spikes, hypothesis-driven experiments and definitions of done that include evaluation thresholds.

The credibility to advise senior stakeholders, translating complex technical trade-offs and statistical findings into clear, commercially grounded decisions, and being plain about what the data does and doesn't support.

A pragmatic, delivery-first mindset: you influence what gets built, then make sure it ships.

Genuine curiosity about where AI is heading, and the judgement to know which developments actually matter for the business.

Desirable

Experience across the wider Azure AI and data estate, such as AKS, Azure AI Search, Azure OpenAI and Azure Document Intelligence.