Gen AI Solutions Lead

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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