at Accenture
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**AI Large Language Model (LLM) Technology Architect - Associate Manager/Specialist (London/Paris/Berlin)** Design and build advanced AI systems, focusing on LLMs, agents, and context layers. Translate requirements into architectural decisions, selecting design patterns, evaluating frameworks, and making deliberate technology choices. Hands-on role: design, build, test AI system modules, and produce architecture artifacts. Work cross-functionally with data engineers, ML engineers, and developers.
- Compensation
- Not specified
- City
- London, Paris, Berlin
- Country
- United Kingdom, France, Germany
Currency: Not specified
Full Job Description
AI Large Language Model (LLM) Technology Architect
Career Level: Associate Manager / Specialist
Location: London/Paris/Berlin
YOU ARE
As a hands-on AI/LLM Architect, you will be at the heart of designing and building advanced AI systems that power the modern enterprise. This is a deeply technical, hands-on role you will spend the majority of your time in the architecture and engineering of real-world AI solutions across classical machine learning, generative AI, and agentic systems, delivering these within active client engagements.
You will translate requirements into concrete architecture decisions: selecting design patterns, evaluating and benchmarking technical frameworks, assembling reusable components, and making deliberate technology choices that balance innovation with enterprise-grade reliability. You will design and build AI agent architectures including multi-agent orchestration, tool use, skills use, and memory systems and work hands-on with foundation models through fine-tuning, retrieval-augmented generation (RAG), and custom model integration. A part of your work will also involve engineering the AI context layer that makes these systems intelligent in practice connecting enterprise knowledge bases, structured and unstructured data sources, and domain-specific content so that AI outputs are grounded, accurate, and relevant to the client's business. You will design and validate systems against enterprise non-functional requirements across security, observability, governance, performance, and scalability. A core output of this role is the production of tangible engineering and architecture deliverables. This means writing and owning software components building, integrating, and testing AI system modules as a practitioner alongside producing detailed architecture artifacts including architecture decision records (ADRs), component diagrams, data flow diagrams, and integration specifications that guide and enable broader engineering teams.
You will work with cross-functional delivery teams alongside data engineers, ML engineers, and application developers, and this role is an opportunity to develop deep expertise across the full AI architecture stack, sharpen your engineering instincts on complex, real-world problems, and build a foundation for growing into a lead or principal architect over time.
THE WORK
Independently design, build, and deliver software components across the AI architecture owning them end to end from design through implementation, integration, and testing as a hands-on practitioner
Design and build AI agent architectures including individual agents, their prompts, tools, and skills, multi-agent orchestration, and memory systems making deliberate design pattern and technology choices
Design and implement agent orchestration patterns that handle task handoffs, communication, state management, and error recovery, validating them through hands-on prototyping
Evaluate multiple design options and technical approaches, making deliberate, justified design choices that balance capability, cost efficiency, performance, and enterprise-grade reliability
Design, build, and run evaluation strategies and harnesses that measure agent and system quality on metrics such as accuracy, relevance, and faithfulness, translating findings into design improvements
Architect and implement foundation model integrations selecting the right models, invocation patterns, and customization approaches (fine-tuning, RAG, custom integration) based on capability, cost, and performance trade-offs
Design and build model adaptation and fine-tuning pipelines, applying working knowledge of transformer-based architectures to inform model selection and optimization
Design and build the AI context layer including context graph design and ingestion pipelines that parse, chunk, enrich, and index structured and unstructured enterprise content, and the retrieval components that ground AI outputs in the client's knowledge
Build embedding, vector storage, and retrieval (semantic, hybrid, reranking) into end-to-end RAG pipelines, applying integration patterns that connect to enterprise data sources
Design and implement context assembly and memory components that manage prompts, context windows, and conversational state for grounded, accurate outputs
Identify, design, and build reusable components and solution patterns that accelerate delivery and can be templated across engagements
Design for cost efficiency and performance optimizing model usage, inference patterns, caching, and resource utilization to meet target latency, throughput, and cost objectives
Design, build, and validate systems against enterprise non-functional requirements implementing guardrails, prompt-injection defenses, PII handling, and access controls for security and Responsible AI
Build governance controls including versioning, audit logging, and lineage tracking, and produce the model documentation that keeps systems auditable
Build observability into systems logging, tracing, monitoring, alerting, and cost tracking to ensure AI solutions remain healthy, performant, and scalable in production
Produce detailed architecture artifacts including architecture decision records (ADRs), architecture blueprints, design documents, agent orchestration and integration pattern specifications, component and data flow diagrams that guide and enable broader engineering teams
Continuously learn, evaluate, and apply new design patterns, frameworks, and technologies across the fast-evolving AI landscape, balancing innovation with enterprise-grade reliability
Collaborate with cross-functional delivery teams data engineers, ML engineers, and application developers to translate requirements into concrete architecture decisions that meet stakeholder needs
