AI Large Language Model (LLM) Technology Architect

Overview

In London, you will lead hands-on AI/LLM solution design and delivery for enterprise clients. You’ll shape multi-model architectures, agentic systems, and grounding layers that connect enterprise data with AI outputs. This role blends engineering with architecture artifacts to enable scalable, responsible AI across client engagements. You’ll work with cross-functional teams and evolve patterns that balance innovation with reliability, offering a clear path to senior architectural leadership.

Responsibilities

• Design and deliver software components end-to-end as a hands-on practitioner
• Create AI agent architectures, prompts, tools, skills, and memory structures, plus multi-agent orchestration
• Develop agent orchestration patterns with task handoffs, state management, and error handling and prototype them
• Evaluate design options to balance capability, cost, performance, and reliability
• Build evaluation strategies to measure accuracy, relevance, and faithfulness and translate results into design improvements
• Architect integrations with foundation models (finetuning, RAG, custom integration) and optimize selection and usage
• Design and implement model adaptation and fine-tuning pipelines using transformer-based knowledge
• Develop the AI context layer (context graph, ingestion, enrichment, indexing, retrieval) grounded to enterprise content
• Implement embedding/vector storage and retrieval (semantic/hybrid) in end-to-end RAG pipelines connecting to data sources
• Create context assembly and memory components for grounded, accurate outputs
• Identify reusable components and patterns for templated delivery across engagements
• Optimize for cost, latency, throughput, and resource utilization while meeting enterprise objectives
• Design and validate systems against security, governance, PII handling, and guardrails for Responsible AI
• Build governance, versioning, audit logging, and data lineage; document models for auditable systems
• Incorporate observability (logging, tracing, monitoring, alerting, cost tracking) to maintain production health
• Produce ADRs, architecture blueprints, design docs, and integration patterns to guide engineering teams
• Continuously learn and apply new AI patterns and technologies balancing innovation with reliability
• Collaborate with data engineers, ML engineers, and application developers to translate requirements into architecture decisions

Key requirements

• Proven experience designing and deploying enterprise-grade AI solutions using agentic, generative, and classical AI/ML on at least one cloud vendor
• Practical experience in Agentic, LLM, and Generative AI space
• Proficient coding in Python
• Strong foundation in architecting and operationalizing LLM-driven application architectures
• Professional experience in ML, DL, NLP, and engineering of AI solutions
• Several years of hands-on ML architecture experience designing large-scale data and analytics systems

• Cross-functional collaboration
• Analytical thinking
• Problem solving
• Python
• AI architecture design for LLMs and agent-based systems
• Agentic and Generative AI

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