Enterprise AI Architect

Description

You will shape client-specific AI architectures that connect agents, models, enterprise data, applications, identity and controls across cloud and on-premises environments.

The role sits within the company's AI Transformation & Agentic Systems Practice and works with client business owners, product owners and technology teams.

Hybrid working.

Responsibilities

• Turn business and product needs into target architectures and concrete implementation choices.
• Design agent, retrieval-augmented generation, model routing, integration, API, identity and human-in-the-loop patterns.
• Set out hybrid deployment approaches that balance data residency, latency, security, performance and cost.
• Compare technology options and record architecture decisions, the trade-offs behind them and non-functional requirements.
• Give technical assurance across delivery and lead production-readiness reviews.
• Work with governance, Responsible AI, cyber, privacy and sector specialists.
• Support proposals, client workshops and market development in line with your seniority.
• Build reusable methods, patterns, code, assets and lessons learned, and coach colleagues.
• Take part in the practice's continuous learning programme.
• Meet the company's quality, independence, confidentiality and risk management standards.

Requirements

• At least 8 years in solution, enterprise, cloud or AI architecture.
• Solid understanding of generative AI, agentic systems, data platforms, integration and distributed applications.
• Experience designing solutions that combine hybrid cloud and on-premises infrastructure.
• Able to explain architecture choices to executives, engineers, security teams and business owners.
• Hands-on experience with at least two agent orchestration approaches, such as LangChain and LangGraph, Microsoft Agent Framework or Semantic Kernel, OpenAI Agents SDK, AutoGen, CrewAI, or comparable frameworks.
• Able to design deterministic and agentic workflows, single-agent and multi-agent setups, durable state, memory, tool calling, hand-offs, human approval, and fallback and exception handling.
• Strong grasp of RAG and knowledge architectures: embedding models, vector and hybrid search, reranking, metadata filtering, semantic layers, knowledge graphs, context management and retrieval evaluation.
• Experience with model-agnostic and multi-model designs across managed and self-hosted models, covering model routing, gateways, prompt and policy layers, structured outputs, caching, and latency and cost trade-offs.
• Practical knowledge of MCP and API-based tool integration, event-driven architecture, identity delegation, secrets management, auditability and zero-trust patterns for agents.
• Track record producing architecture artefacts for hybrid deployments using cloud AI platforms, containers, Kubernetes, private networking, on-premises data sources and locally hosted inference where needed.

Conditions

Hybrid working.

The company is an equal opportunity employer.