AI Architect - SC cleared - London/Hybrid - Government project
Key Responsibilities
- Architect enterprise-grade AI solutions using frontier foundation models (eg Claude, GPT, Gemini family) across use cases including agentic automation, retrieval-augmented generation (RAG), copilots, and multi-modal applications
- Lead technical solutioning during pre-sales and discovery, translating client business challenges into scalable AI architecture and delivery roadmaps
- Design and govern the AI platform architecture: model orchestration, prompt and context management, retrieval pipelines, vector stores, agent frameworks, and tool/context integration (including emerging protocols such as MCP)
- Define and enforce responsible AI, governance, and assurance frameworks - covering bias, safety, data privacy, and model risk - aligned to client and regulatory requirements
- Lead and mentor a team of AI engineers and architects, building technical capability and clear career pathways within the practice
- Own technical relationships with hyperscaler and frontier model partners (Azure OpenAI, AWS Bedrock, Google Vertex AI, Anthropic, OpenAI) to stay ahead of platform capability
- Drive reusable accelerators, frameworks, and IP that can be repeated and scaled across client engagements
- Act as a trusted advisor to client CxOs and senior stakeholders on AI strategy, architecture, and roadmap
- Own end-to-end delivery quality: architecture reviews, technical governance boards, production readiness, and post-go-live scaling
- Contribute to thought leadership - whitepapers, conference contributions, and internal capability-building
Required Skills & Experience
- 10+ years in software engineering, data engineering, or solution architecture, including at least 3-5 years focused specifically on applied AI/ML or generative AI
- Proven, hands-on architecture experience with frontier large language models and multi-modal models - including prompt engineering, fine-tuning, RAG, and agentic/multi-agent system design
- Strong grounding in LLMOps/MLOps: evaluation frameworks, observability, cost and latency optimisation, guardrails, and safe deployment at enterprise scale
- Direct experience with major frontier model providers (Anthropic Claude, OpenAI, Google Gemini, or equivalent) and cloud AI ecosystems (Azure AI/OpenAI Service, AWS Bedrock, Google Vertex AI)
- Solid understanding of AI governance, responsible AI, and emerging regulatory frameworks (eg EU AI Act, NIST AI RMF, or regional equivalents)
- Demonstrated technical leadership - leading architecture teams, chairing design/technical governance authorities, mentoring senior engineers
- Client-facing consulting experience: solutioning, pre-sales support, and stakeholder management at senior client level
- Strong software engineering fundamentals: API design, cloud-native architecture, microservices, and data pipeline/data engineering experience
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent professional experience; an advanced degree is a plus
Preferred/Desirable Skills
- Experience with agent orchestration frameworks (eg LangGraph, AutoGen, CrewAI) and emerging tool-integration protocols (eg Model Context Protocol)
- Public sector or other regulated-industry delivery experience (financial services, healthcare, government)
- Relevant certifications (eg Azure AI Engineer Associate, AWS Certified Machine Learning, Google Professional ML Engineer)
- Experience building or scaling an AI Centre of Excellence or comparable practice capability within a consulting organisation
- Published thought leadership, patents, or conference speaking in AI/ML
