AI/LLM Technology Architect
Accenture
AI/LLM Technology Architect
Accenture
Advises businesses and runs their technology
AI Large Language Model (LLM) Technology Architect
Career Level: Associate Manager / Specialist
Location: London
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 spendthe majority ofyour 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,evaluatingand 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 partof 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 andvalidatesystems 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),componentdiagrams, 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,validatingthem 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,accurateoutputs
Identify, design, and build reusable components and solution patterns that accelerate delivery and can be templated across engagements
Design for cost efficiency and performance —optimizingmodel usage, inference patterns, caching, and resourceutilizationto meet target latency, throughput, and cost objectives
Design, build, andvalidatesystems 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 solutionsremainhealthy, performant, and scalable in production
Produce detailed architecture artifacts — including architecture decision records (ADRs), architecture blueprints, design documents, agent orchestration and integration pattern specifications,componentand data flow diagrams — that guide and enable broader engineering teams
Continuously learn, evaluate, and applynew designpatterns, 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
EDUCATION
Bachelor's Degree or equivalent
BASIC (REQUIRED) QUALIFICATION
Proven experience in designing & deploying enterprise grade advanced ai solutions using agentic,generativeand classical AI/ML using at least one cloud vendor.
Practical experience in the Agentic,LLMand Generative AI space.
Well versed in coding using python
Solid foundation in architecting and operationalizing LLM driven application architecture patterns.
Professional working experience in coding engineering, machine learning, deep learning and NLP solutions and applications.
Several years of hands on experience as a machine learning architect in the industry designing bigdata,machine learning. large scale analytical engineering solutions.
About Accenture
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.Visit us atwww.accenture.com
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Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.
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