Applied AI Engineer

Overview

In this role you design, build, and productionise AI-enabled solutions for legal and business services. You will translate business problems into technical designs, develop reusable AI components, and integrate tools across internal systems. Your work supports secure, scalable AI workflows and governance within a cross-functional team. This is an opportunity to shape how AI accelerates client service delivery and internal efficiency at a global law firm.

Responsibilities

• Design, build and maintain AI-enabled applications, automations and internal tools using Python, cloud services and enterprise platforms
• Interview stakeholders to understand problems and translate requirements into practical technical designs
• Create integrations between internal systems, enterprise platforms, external applications and AI services
• Develop reusable AI skills, agents, workflow components and automation patterns for firm-wide adoption
• Develop document-focused AI workflows (extraction, summarisation, search, retrieval, due diligence support)
• Productise AI pilots with improved reliability, testing, documentation and reuse
• Design multi-step AI workflows including tool-using, agentic and platform-native automations
• Collaborate with product, UX, engineering, cloud, innovation and business teams to deliver secure, production-ready solutions
• Apply governance around confidential client data, privacy, security and responsible AI

Key requirements

• Python software engineering experience across applications, integrations, automations or internal tools
• Experience implementing AI/ML or LLM-enabled solutions in production or near-production settings
• Experience with APIs, SDKs, cloud services and enterprise platforms
• Understanding of prompt engineering, RAG, LLM workflows or agentic patterns
• Ability to interview stakeholders and translate business processes into technical designs
• Experience with testing, debugging, documentation, Git and maintaining reliable systems
• Strong communication and problem-solving skills across technical and non-technical audiences

• Consultative approach
• Collaborative mindset
• Problem-solving
• Python
• AI/ML/LLM in production
• APIs and SDKs

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