Chief AI Engineer

Chambers; Greater London; Presencial; GBP 150.000 - 240.000; Jornada completa

Descripción de la vacante

Chambers and Partners is seeking a Chief AI Engineer to lead our AI engineering function. You will own technical direction across AI architecture, model selection, data strategies and evaluation, while hands-on coding and problem solving remain part of your remit.

You will shape the AI strategy, advise the executive team, and drive production-grade AI work across legal market intelligence with a strong emphasis on rigour, governance and impact.

Formación

• Significant experience in senior AI/ML leadership roles with end-to-end ownership.
• Proven ability to design and deploy AI systems at scale in production environments.
• Strong foundations in ML/DL, NLP, and modern AI toolchains.

Responsabilidades

• Lead end-to-end AI lifecycle from ideation to production deployment and monitoring.
• Set technical direction across architecture, data strategies and evaluation disciplines.
• Mentor and grow the AI engineering team; build high quality, auditable AI systems.

Conocimientos

Senior AI leadership

AI architecture

ML/DL

Python

SQL

Azure OpenAI

MLOps

Natural Language Processing

Experimentation

Herramientas

Azure ML

Databricks

Transformers

MLflow

Descripción del empleo

AI is already making a real impact at Chambers and Partners, with solutions running in production across our editorial, research, product and commercial teams. We’re now looking for a Chief AI Engineer to lead our AI engineering function, own the technical direction of AI across the business and shape the next stage of our AI transformation.

This is a deeply technical leadership role. You’ll be part of the Senior Leadership Team and be the final technical authority on how we build, evaluate, deploy and run AI: the AI architecture, modelling choices, engineering standards and evaluation discipline the team works to. You’ll have taken AI systems end to end many times over, from ideation and discovery through to deployment, monitoring and continuous improvement, and you’ll stay close enough to the code to solve the hardest problems yourself.

Alongside this, you’ll help set our AI strategy and roadmap, advise our Executive Leadership Team on where AI creates the greatest value, and represent Chambers as a credible voice on AI in the legal market. Around 70% of the role is hands‑on technical leadership and delivery; around 30% is strategic.

Main Duties and Responsibilities:

Technical leadership and delivery (c.70%)

• Own the end-to-end lifecycle of our AI solutions, from ideation, discovery and scoping through experimentation, production deployment, monitoring and iteration, measuring their impact against agreed business outcomes.
• Act as the technical design authority for AI, setting direction across architecture, model selection, data and retrieval strategies, fine‑tuning, evaluation and deployment, and making the hard calls on whether to build, buy or not use AI at all.
• Stay hands‑on: prototype, review code, pair on the hardest problems and personally lead the design of business‑critical AI systems.
• Design scalable, secure and maintainable AI solutions in partnership with our Technology and Data Platform teams.
• Define and own evaluation frameworks for ML, LLM and GenAI outputs, covering accuracy, consistency, bias mitigation and regression testing, so that everything we release meets Chambers’ standards for rigour and independence.
• Establish production discipline across MLOps and LLMOps: experiment tracking, model registry, monitoring, drift management, observability and safe, structured and streamlined deployment.
• Lead, mentor and develop our AI engineering team, hiring strong engineers, raising the technical bar and creating an environment where people do their best work. Ensure high quality engineering practices, including code review and safe, structured and streamline deployment.
• Run delivery across multiple concurrent AI workstreams, managing cross‑team dependencies with Product, Research, Commercial, Data and Technology teams.

Strategic leadership (c.30%)

• Shape and own the AI strategy and roadmap with the Chief Data and AI Officer, aligned with Chambers’ business and data strategies.
• Act as a senior adviser to the Executive Leadership Team on AI opportunities, risks and trade‑offs, translating complex technical choices into clear, commercially grounded decisions.
• Establish prioritisation frameworks that balance innovation, research integrity, risk and commercial impact, and identify where AI can power new products, features and services.
• Partner with Research leadership to augment expert judgement with AI, improving the consistency, scalability and depth of our research outcomes.
• Keep Chambers ahead of developments in AI, evaluate and manage strategic partnerships with AI vendors and platforms, and represent Chambers externally as a credible voice on AI in legal market intelligence.

Why you should apply

This is a highly technical role with a seat on our senior leadership team. You won’t be advising on AI from the sidelines. You’ll sit alongside the leaders who set the direction of the business, bring a genuine engineering perspective to decisions about where we invest and how we compete, and see those decisions through to delivery.

Few organisations can offer data like ours. More than 350,000 research interviews and surveys a year, and a deep, proprietary view of the global legal market, create problems that can’t be solved off the shelf. That’s a real chance to build AI that is genuinely differentiated, not a thin layer over someone else’s model, for a professional audience that relies on it being right.

You’ll have the autonomy to set the standards, build the team and choose the approach, and the backing to make it happen. If you want senior‑level influence without giving up the hard technical work, this is the role.

Skills, Experience, and Personal Attributes:

We’re looking for a Chief AI Engineer leader who has come up through data science, machine learning or software engineering and who combines deep modelling expertise with production engineering excellence. You’ll have taken many AI systems from ideation through to production and monitoring, and gone on to lead engineering teams and influence at senior leadership level. You’ll combine deep technical credibility with the gravitas to earn the confidence of engineers and senior leaders alike.

You’ll bring:

• Significant experience in senior applied AI, ML or data science leadership roles, with a track record of owning AI end to end (ideation, discovery, experimentation, production deployment, monitoring and iteration) across multiple systems that delivered measurable business outcomes.
• 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 (LLMs, 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 remain hands‑on and 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.
• Hands‑on experience securing LLM and agentic systems in production, including threat modelling, red‑teaming, guardrails and controls against prompt injection, data leakage and model misuse.
• A track record as the technical authority for AI: owning decisions across model selection, data and retrieval strategy, evaluation and production deployment, and shaping solution architecture on modern data platforms.
• Commercial discipline in running AI at scale: managing inference cost, latency and performance trade‑offs, and making clear build‑versus‑buy decisions grounded in unit economics.
• Lead the design and evaluation of agentic AI systems, including orchestration, tool use and interoperability standards such as the Model Context Protocol (MCP), so that our data and AI capabilities can be safely accessed across internal tools and external platforms.
• A deep understanding of model risk, bias, explainability and responsible‑AI principles, and how to build them into systems used in trusted, high‑stakes contexts.
• Proven leadership of AI, ML or data science teams: hiring, mentoring, performance management 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.
• Define and report the value AI delivers, setting clear measures of impact on revenue, efficiency and quality, and presenting progress, risks and returns to the Executive Leadership Team.
• Gravitas and executive presence: a track record of influencing at executive level, advising senior leaders on AI opportunities, risks and trade‑offs, and being plain about what the data and the models do and don’t support.
• Strategic and commercial acumen: experience shaping an AI strategy or roadmap and linking AI to product strategy, research outcomes, customer value or revenue growth.
• 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.
• 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.
• 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.
• Experience in data‑rich, research‑driven or professional services organisations, ideally in legal, information services or market intelligence.
• Experience designing human‑in‑the‑loop AI workflows with expert users, where AI augments rather than replaces professional judgement and decision‑making stays with domain specialists.
• A track record of driving AI adoption and literacy across an organisation, through enablement, education and engagement that builds confidence and capability beyond the technical team.
• Experience representing an organisation externally on AI, for example through speaking, writing or industry forums.

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.