Anthropic certified AI Large Language Model (LLM) Technology Architect

Accenture PLC; Greater London; Sur place; GBP 120 000 - 190 000; Plein temps

Résumé du poste

Accenture PLC in London is seeking an Anthropic-certified AI LLM Technology Architect to design and implement enterprise AI systems across generative, agentic, and classical AI. You will lead hands-on architecture, develop reusable components, and guide client engagements with architecture decisions and ADRs.

You will collaborate with data engineers, ML engineers, and application developers, build grounding context layers, manage RAG pipelines, and ensure security, observability, and governance

Qualifications

• Anthropic certification required.
• Experience designing & deploying enterprise AI solutions using agentic, generative and classical AI/ML.
• Proficient in Python and building robust AI architectures.
• Strong background in grounding AI outputs with enterprise data and governance.

Responsabilités

• Design, build, and deliver end-to-end AI system components within client engagements.
• Architect agent orchestration patterns, tool usage, and memory systems for robust AI agents.
• Evaluate design options, ensuring cost efficiency, performance, and reliability.
• Develop and validate ADRs, data flow diagrams, and integration specifications for teams.

Connaissances

Anthropic certification

Python

LLM architecture

Agentic AI

Cloud architecture

NLP solutions

Machine learning

Deep learning

Formation

Bachelor's Degree or equivalent

Description du poste

Anthropic certified AI Large Language Model (LLM) Technology Architect

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

• Hold an active Anthropic certification

• 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.