Lead Engineer (AI Platforms)

As Lead Engineer (AI Platforms), you will help design and deliver the next generation of AI-enabled work at G+T. This is a senior, hands-on role for someone who can combine strong engineering judgment with a clear point of view on how AI is changing the way knowledge work is designed, expressed and automated.

You will work across operations and legal use cases to identify high-value opportunities, shape practical technical approaches, and build agentic solutions that are secure, reliable and useful in practice. You will help set direction for how we use models, tools, skills and orchestration patterns across the firm, while also staying close to the work itself: prototyping, building, refining and helping ship.

This role will suit someone who is already AI-native in how they operate: someone who uses modern coding agents and AI workflows as a first-class part of engineering, and who wants to help translate that way of working into enterprise capability.

About you

You are a strong engineer with a deep interest in what AI means for the future of enterprise work. You see AI not simply as a productivity layer, but as a new way of designing, expressing and automating complex workflows.

You are highly curious, imaginative and practical. You can move quickly from idea to implementation, but you also understand what it takes to build systems that are secure, reliable and maintainable in a real operating environment. You stay close to rapid changes in models, tools and techniques, and you have the judgment to distinguish durable shifts from temporary noise.

You are comfortable using natural language, agent instructions and AI coding tools as first-class engineering instruments, while also knowing when a problem requires stronger software structure, abstraction and control. You already work this way yourself, and you can help others adopt those habits in a disciplined, enterprise-ready way.

Key Responsibilities

• Designing and building agentic solutions that combine models, tools, skills, retrieval, workflow automation and secure access to enterprise systems.
• Turning ambiguous requirements into practical designs, delivery plans and working systems that create real value.
• Prototyping quickly, hardening what works, and developing reusable patterns and capabilities that can be applied across multiple use cases.
• Shaping how AI-native engineering is applied in practice across the team, including the effective use of coding agents, executable specifications and modern AI delivery workflows.
• Building evaluation, guardrails, and reliability practices so AI systems remain robust as models, prompts, tools and workflows evolve.
• Working with technical and non-technical stakeholders to explain options clearly, challenge assumptions when needed, and help drive good decisions on AI direction, adoption and delivery.

Key Skills and Attributes

• Deep hands-on experience building with frontier AI coding tools, such as Claude Code, Codex or equivalent, and using them to materially accelerate design, development, testing and iteration. Comfortable treating natural language, agent workflows, and executable specifications as part of the engineering stack, not just as an assistive layer.
• Proven experience designing and delivering production software systems and enterprise AI solutions, with practical expertise in agentic patterns such as tool use, orchestration, retrieval, workflow automation, and human-in-the-loop design.
• Strong understanding of MCP, Skills and how these fit into scalable enterprise architecture.
• Ability to design AI systems that remain robust as the technology evolves, with a clear grasp of evaluation, generalisation, abstraction, policy, guardrails and related reliability considerations.
• Experience with cloud platforms, enterprise integrations and modern production engineering practices.
• Familiarity with privacy, data residency and compliance considerations relevant to enterprise AI adoption

Qualifications and Experience

• Experience assessing AI vendors, models or platforms and making practical recommendations on where they fit in an enterprise environment.
• Experience working in legal, professional services, financial services or another environment with strong confidentiality, governance and risk expectations.
• Degree, Masters or PhD in Data Science, AI or a related quantitative field preferred but not mandatory.

Please submit your interest via our career's website with a resume.

We are committed to providing and maintaining a diverse and inclusive environment and a culture where everyone feels valued and empowered to contribute.