AI Large Language Mode (LLM) Technology Lead/Principal Architect in London

As a Lead or Principal AI Architect, you will be the definitive technical authority on AI architecture within client engagements and across the practice. Your responsibilities include partnering with CIOs, CTOs, and senior business leaders to shape and articulate enterprise AI strategy, leading assessments and building implementation roadmaps, owning the complete end‑to‑end AI technical solution, translating architectural principles into defensible designs, and delivering actionable prototypes. You will guide cross‑domain engineering teams, integrate domain architects and specialists, establish governance frameworks and security standards, manage model and inference strategies, and represent the firm externally through thought leadership, publications, and conference engagements.

Responsibilities

• Partner with CIOs, CTOs, and business leaders to shape the enterprise AI strategy and connect business goals to a coherent technical vision.
• Lead enterprise AI assessments and build enterprise AI implementation road‑maps that sequence investments for lasting competitive advantage.
• Own the complete, end‑to‑end technical solution for complex AI platforms, ensuring every domain is cohesively designed and aligned to business objectives and enterprise standards.
• Translate governing architecture principles into a concrete, defensible technical solution that domain teams build against.
• Build innovative prototypes and proofs of concept hands‑on, using emerging technologies to de‑risk decisions and prove value early.
• Perform technology assessments and comparisons, making definitive, evidence‑based recommendations on tools, frameworks, and platforms.
• Set the architectural direction for model‑ and tool‑agnostic multi‑agent ecosystems governed through a registry‑bound AI Gateway.
• Establish the agent registry and certification model that mandates no uncertified agent reaches production.
• Define memory as a first‑class abstracted platform service, decoupled from any underlying vendor engine.
• Define the foundation model and inference strategy—adaptation, fine‑tuning, and dynamic cost/quality/latency‑aware routing.
• Set the standards for high‑throughput, low‑latency inferencing and classical ML deployment within unified, production‑ready platforms.
• Own the architecture of the enterprise context layer—knowledge graphs, ontologies, vector search, and semantic retrieval—grounding the solution in client knowledge.
• Set the design direction for context assembly and memory that manages prompts, context windows, and conversational state across the platform.
• Be accountable for security, governance, observability, performance, and scalability addressed holistically and consistently across every domain.
• Establish the identity and authorization model—per‑agent identity, IAM/IAP binding, and defense‑in‑depth enforcement.
• Define the layered guardrail framework applied at every boundary, balancing protection with performance.
• Govern the MCP control plane—registry, gateway, and risk scoring—across all internal and third‑party servers.
• Mandate adopt‑over‑build for productized evaluation and observability stacks.
• Establish FinOps as a first‑class concern—usage labelling, gateway‑enforced budgets, and cost‑per‑archetype as a planning input.
• Make definitive decisions on design patterns, reference architectures, frameworks, and technology selections, balancing innovation with pragmatism.
• Lead and integrate the work of domain architects and specialists, resolving cross‑domain tensions into a unified, enterprise‑ready system.
• Build the practice's reusable reference architectures, frameworks, and assets, with a deliberate adopt‑over‑build stance.
• Conduct deep‑dive architecture workshops and working sessions with client executives and engineering teams.
• Produce and govern the authoritative architecture artifacts—blueprints, reference architectures, ADRs, and integration specifications—that guide delivery at scale.
• Serve as a recognized thought leader in AI, shaping the practice's point of view and representing the firm externally through publications and conference engagements.

Qualifications

Education

• Bachelor's Degree or equivalent

Required Skills / Experience

• Extensive experience in designing & deploying enterprise‑grade advanced AI solutions using agentic, generative and classical AI/ML on at least one cloud vendor.
• Proven experience in the LLM and Generative AI space.
• Well‑versed in architecting and operationalizing LLM‑driven application architecture patterns.
• Deep experience in engineering, machine learning, deep learning and NLP solutions and applications.
• Several years of hands‑on experience as a machine architect designing big‑data, machine learning and large‑scale analytical engineering solutions.