Chief AI Engineer

Overview 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 & 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: 

Essential 

• 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: class