Herbert Smith Freehills
Emerging Technology Delivery Lead
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Herbert Smith Freehills Kramer is a world-leading global law firm, where our ambition is to help you achieve your goals.
Exceptional client service and the pursuit of excellence are at our core. We invest in and care about our client relationships, which is why so many are longstanding. We enjoy breaking new ground, as we have for over 170 years.
As a fully integrated transatlantic and transpacific firm, we are where you need us to be. Our footprint is extensive and committed across the world’s largest markets, key financial centres and major growth hubs.
At our best tackling complexity and navigating change, we work alongside you on demanding litigation, exacting regulatory work and complex public and private market transactions. We are recognised as leading in these areas.
We are immersed in the sectors and challenges that impact you. We are recognised as standing apart in energy, infrastructure and resources. And we’re focused on areas of growth that affect every business across the world.
All of this is achieved by supporting the growth of our people, who help us deliver on our ambition – which is to help you achieve yours.
Herbert Smith Freehills Kramer: Your goals. Our ambition
The Opportunity
Role Summary
Emerging Technology Delivery Lead is a senior hands-on technology leadership role responsible for designing, operationalising and scaling enterprise-grade AI, data and intelligent platform capabilities across the firm.
The role bridges execution, translating emerging technology vision, architecture blueprints and innovation initiatives into secure, scalable and supportable enterprise solutions.
Reporting into and working closely with the Principal Strategist Emerging Technologies, the role is accountable for shaping and delivering intelligent platform architectures that enable AI-assisted workflows, advanced analytics, semantic capabilities, automation and knowledge-driven services.
The role combines deep technical expertise with strong delivery leadership and engineering collaboration to ensure innovative solutions move successfully from experimentation into enterprise adoption.
Primary Responsibilities
- Participate in day scrum, manage ADO board with Scrum Master
- Design and build scalable enterprise AI and intelligent platform architectures aligned to enterprise strategy and governance standards
- Define reference architectures, patterns and integration models supporting the System of Intelligence (SOI)
- Lead design for:
- RAG and GraphRAG solutions
- Knowledge graphs and semantic platforms
- AI orchestration and agentic workflows
- AI integration layers and APIs
- Vector databases and retrieval platforms
- AI-assisted workflow automation
- Ensure AI platforms integrate effectively with enterprise systems, identity models, security controls and data platforms
- Define reusable architecture patterns and engineering standards for AI-enabled solutions
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Delivery & Engineering Enablement
- Work closely with engineering, platform, security and data teams to operationalise intelligent platform capabilities
- Lead technical delivery alignment from proof-of-concept through production adoption
- Support engineering teams with architecture guidance, implementation oversight and technical governance
- Establish scalable delivery patterns for AI solutions across cloud and hybrid environments
- Ensure solutions are observable, supportable, resilient and operationally sustainable
- Accelerate transition from innovation initiatives into business-as-usual services
AI Governance & Responsible AI
- Ensure AI capabilities align with security, privacy, compliance and governance requirements
- Embed responsible AI principles into architecture patterns and delivery processes
- Define runtime controls, monitoring, traceability and human-in-the-loop governance mechanisms
- Support AI inventory management, model governance and architectural traceability
- Contribute to AI risk assessments, threat modelling and regulatory alignment activities
- Ensure alignment with emerging regulatory frameworks including EU AI Act, NIST AI RMF and enterprise governance standards
Data, Semantic & Knowledge Architecture
- Support development of enterprise semantic and knowledge capabilities
- Define architecture approaches for:
- enterprise ontologies
- semantic models
- metadata-driven platforms
- knowledge graph integration
- unstructured data processing
- Ensure trusted and governed data foundations underpin AI-enabled capabilities
- Collaborate with data architecture and engineering teams to align AI and data strategies
Innovation & Emerging Technology Enablement
- Evaluate emerging technologies, tools and platforms relevant to intelligent systems and enterprise AI
- Support innovation initiatives and experimentation activities within the Innovation Lab
- Help mature experimental solutions into scalable enterprise capabilities
- Contribute to strategic technology roadmaps and capability evolution plans
- Maintain awareness of external market trends, vendor ecosystems and AI platform evolution
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Qualifications, skills and experience
- Extensive experience in building technology solutions including architecture, AI platforms or advanced technology delivery roles
- Strong hands-on experience designing enterprise-grade AI and data platform architectures
- Experience operationalising AI or advanced analytics solutions within regulated or complex environments
- Experience translating proof-of-concepts into scalable enterprise services
- Strong understanding of modern cloud-native architecture patterns
- Experience working across architecture, engineering, security and operational teams
- Experience influencing senior stakeholders within matrixed organisations
Technical skills
- AI orchestration and agentic workflow architectures
- RAG and GraphRAG architectures
- Knowledge graphs, ontologies and semantic technologies
- Vector databases and semantic retrieval patterns
- Microsoft Fabric, Azure AI and modern cloud platforms
- API and integration architecture
- Enterprise security and identity models
- Threat modelling and AI governance
- Observability and operational monitoring patterns
- Structured and unstructured data architectures
- Model lifecycle management and AI operationalisation
- DevSecOps and platform engineering principles
- Applied data science techniques (classification, clustering, NLP)
- Model lifecycle management (training, validation, monitoring)
- Translating legal and business problems into analytical use cases
- Understanding limitations and risks of statistical and ML models
- Large Language Models (LLMs) and GenAI patterns
- RAG architectures, agentic workflows and orchestration
- AI service integration via APIs and model abstraction layers
- Runtime guardrails, monitoring and human in the loop controls
- AI governance frameworks and risk assessment
- Maintaining AI inventories and architectural traceability
Team
Information Technology
Working Pattern
Full time
Location
London
Contract type
Permanent Contract
Diversity & Inclusion
We are committed to attracting people from all backgrounds and creating a respectful and inclusive culture where everyone thrives. We see this as essential to our success, including our ability to innovate and achieve sustained high performance. This is a key part of our Values—Human, Bold, and Outstanding.
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