AI Architect

Overview

As an AI Solution Architect, you will lead the design and delivery of scalable, secure AI systems across client and internal projects. You bridge business needs with data science, software engineering and cloud infrastructure to deliver measurable value. You’ll guide discovery, architect end-to-end AI solutions, and oversee risk, compliance, and deployment. You work closely with cross-functional teams to translate challenges into practical AI strategies, driving responsible AI adoption and impact at scale. This role offers the chance to shape AI-enabled transformations within a respected technology partner that values collaboration and innovation.

Pay / Benefits

• flexible/remote working
• pension
• private healthcare
• life assurance
• gym discounts
• Mindfulness Workshops

Responsibilities

• Lead discovery workshops and requirements gathering with stakeholders
• Design end-to-end AI solution architectures for Generative AI, ML, and Intelligent Automation
• Define integration patterns across enterprise apps, APIs, databases, and cloud services
• Create architecture diagrams, solution designs, and implementation roadmaps
• Evaluate AI technologies and platforms to recommend best-fit solutions
• Advise on AI adoption strategies and use cases
• Conduct feasibility assessments, PoCs, and technology evaluations
• Develop responsible AI frameworks covering bias, explainability, privacy, and regulatory compliance
• Identify opportunities for process optimization via AI and automation
• Provide technical leadership across project lifecycles
• Collaborate with AI/ML engineers, data scientists, software engineers, and product teams
• Review designs for enterprise architecture alignment and support deployment planning

Key requirements

• 7+ years in software engineering, solution architecture, data engineering, or AI-related roles
• 3+ years designing enterprise AI/ML solutions
• Experience leading cross-functional teams and client engagements
• Strong understanding of ML and Generative AI architectures
• Experience with LLMs and Retrieval-Augmented Generation (RAG)
• Knowledge of AI orchestration frameworks: LangChain, LlamaIndex, Semantic Kernel, CrewAI
• Cloud platform expertise in AWS, Azure, or Google Cloud
• API design and microservices architecture
• Data architecture and integration experience
• Familiarity with knowledge graphs, vector databases, and knowledge management systems
• Design and scale of LLM-based applications, including multi-model routing and cost/latency optimization
• Prompt engineering, Agentic AI systems, RAG design patterns
• Evaluation/monitoring frameworks for hallucinations, relevance, and production drift
• AI safety, governance, and compliance

• Stakeholder management
• Executive communication
• Collaborative and cross-functional leadership
• Machine Learning
• Generative AI
• LLMs

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