Applied AI Lead

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, and Person 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.
• Data engineering alongside data science, using tools such as Spark, dbt, Azure Data Factory and SQL.
• Exposure to model serving and optimisation, such as vLLM, ONNX Runtime and quantisation.
• Familiarity with annotation and human-in-the-loop tooling.
• Experience with ranking, scoring or recommendation systems, or with survey and research data.
• Responsible-AI practice, such as fairness assessments, model documentation and input to data protection impact assessments.
• A postgraduate degree in a quantitative discipline, or equivalent applied experience.

We welcome applications from people of all backgrounds and experiences. If you’re not sure you meet every requirement, we’d still like to hear from you — we value potential as much as experience.

Depending on the role, we may be able to offer flexible working options such as hybrid working, part‑time or job‑share arrangements, or flexible hours

About us:

Chambers is the leading legal data and intelligence partner for lawyer, firms, and in-house teams.

We conduct over 350,000 research interviews and surveys with in-house counsel every year, and receive 62,000 submissions from 9,000 firms worldwide, giving us unrivalled insight into the legal sector.

This research powers Chambers Rankings - the definitive guide to the best legal talent - and our Intelligence, which delivers the insights helping firms and in-house teams to succeed.

Our independent, rigorous research identifies the exceptional and charts the path to success, enabling legal professionals to see with clarity, decide with confidence, and plan with ambition..

Equal Opportunity Statement:

We are committed to fostering and promoting an inclusive professional environment for all of our employees, and we are proud to be an equal opportunity employer. Diversity and inclusion are integral values of Chambers and Partners and are key in our culture. We are committed to providing equal employment opportunities for all qualified individuals regardless of age, disability, race, sex, sexual orientation, gender reassignment, religion or belief, marital status, or pregnancy and maternity. This commitment applies across all of our employment policies and practices, from recruiting and hiring to training and career development. We support our employees through our internal INSPIRE committee with Executive Sponsors, Chairs and Ambassadors throughout the business promoting knowledge and effecting change.

Applicants who identify as Disabled and/or Neurodiverse will be entitled to an interview if they meet the minimum criteria as specified in the Job Description, additionally we will offer reasonable adjustments to those who require them. Some examples of reasonable adjustments are extra time in assessments, video interviews to combat travel-based issues and advice on expected interview topics/questions.