Job Description

Key Responsibilities

Lead the design and delivery of production-grade GenAI solutions using LLMs, RAG, and vector search, optimising prompts, context, and retrieval for accuracy and performance. Architect and implement GenAI services integrated with enterprise systems and cloud platforms, ensuring scalability, security, and maintainability. Own the delivery of GenAI applications such as internal support assistants, case handling tools, and commercial automation services. Deliver solutions from proof-of-concept to production, focused on measurable business value while meeting compliance and performance standards. Collaborate with cross-functional teams to embed GenAI into operational and commercial workflows as part of wider digital transformation efforts. Lead technical workshops and enablement sessions to support internal adoption of GenAI tools and practices. Evaluate GenAI APIs, frameworks, and vendors, advising on architecture, integration strategy, and build-vs-buy decisions. Implement monitoring and evaluation processes to ensure solution quality, reliability, and continuous improvement. Partner with security and governance teams to ensure responsible, compliant deployment of GenAI technologies.

Skills & Experience

Essential

Proven experience leading the development and deployment of GenAI systems in enterprise environments. Deep hands-on expertise with LLMs, RAG architectures, vector databases, and orchestration frameworks (e.g. LangChain). Strong understanding of prompt design, context management, and GenAI-specific risks such as hallucination and data exposure. Experience integrating GenAI solutions with enterprise systems and cloud platforms (AWS, Azure, or GCP). Proven ability to work independently and collaboratively across technical and business teams to deliver outcomes with clear business impact. Excellent communication and stakeholder engagement skills, particularly in cross-functional environments.

Desirable

Background in software engineering, ML engineering, or AI solution architecture. Familiarity with CI/CD, observability, and MLOps practices that support scalable GenAI deployment. Experience working in large-scale or regulated enterprise settings. Proven track record delivering GenAI solutions across knowledge assistants, customer support, commercial automation, and engineering tools. Understanding of evaluation methods for GenAI in production environments, including user feedback loops, latency tracking, and quality scoring. Exposure to advanced GenAI orchestration patterns and frameworks such as tool use, memory management, agentic workflows, or Model Context Protocol.

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Permanent