AI Large Language Model (LLM) Technology Architect

WeAreTechWomen; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Accenture is seeking an hands-on AI/LLM Architect to design and build enterprise AI systems. You will shape agent architectures, memory, and tool use across client engagements in a technically rigorous role.

You will translate requirements into concrete architecture decisions, evaluate frameworks, and deliver ADRs, diagrams, and integration specifications that guide multi-disciplinary teams in real-world deployments.

Qualifications

• Designing & deploying enterprise-grade AI solutions across agentic, generative and classical AI/ML.
• Hands-on experience with architecture decisions, ADRs and data flows.

Responsibilities

• Architect and implement AI agent architectures and memory systems.
• Design evaluation strategies for model and system quality.
• Collaborate with data engineers, ML engineers, and developers across engagements.

Skills

Agentic AI

Generative AI

LLM architecture

Python coding

Enterprise AI

Education

Bachelor's degree or equivalent

Tools

Python

Job description

Job Description

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 andvalidate 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),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, andvalidate 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 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

Qualification

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.

Locations

London

Additional Information

Equal Employment Opportunity Statement

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Job candidates will not be obligated to disclose sealed or expunged records of conviction or arrest as part of the hiring process.

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About Accenture

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