Anthropic certified AI Large Language Model (LLM) Technology Architect

Accenture; Greater London; On-site; GBP 140,000 - 190,000; Full time

Job summary

Accenture in London seeks an Anthropic-certified AI LLM Technology Architect to lead hands-on design and delivery of advanced AI systems. You will architect agent-based solutions, integrate foundation models, manage context and memory, and drive cost-efficient, scalable implementations in large client engagements.

You will collaborate with data engineers, ML engineers, and application developers to produce ADRs, data flows, and integration specifications, ensuring security and governance across

Qualifications

• Hold an active Anthropic certification.
• Proven experience in designing & deploying enterprise grade AI solutions using agentic, generative and classical AI/ML with at least one cloud vendor.
• Practical experience in the Agentic, LLM and Generative AI space.
• Well versed in coding using Python.
• Strong foundation in architecting and operationalizing LLM-driven applications.
• Professional experience in coding engineering, ML, DL and NLP solutions.
• Several years of hands-on ML architecture experience in large-scale analytics.

Responsibilities

• 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 agents, prompts, tools, skills, multi-agent orchestration, and memory systems.
• Design and implement agent orchestration patterns handling task handoffs, state management, and error recovery, validated by prototyping.
• Evaluate design options balancing capability, cost, performance and reliability.
• Design, build, and run evaluation strategies measuring accuracy, relevance, and faithfulness for improvements.
• Architect and implement foundation model integrations with model selection, invocation patterns, and customization.
• Design and build model adaptation and fine-tuning pipelines for transformer-based architectures.
• Design and build the AI context layer with context graphs, ingestion pipelines, and retrieval to ground outputs.
• Build embedding, vector storage, and retrieval into end-to-end RAG pipelines.
• Design and implement context assembly and memory components for grounded outputs.
• Identify and build reusable components and solution patterns for rapid delivery.
• Optimize for cost efficiency and performance, including caching and resource utilization.
• Design, build, and validate systems against enterprise non-functional requirements including security and Responsible AI.
• Build governance controls with versioning, auditing, and lineage tracking and model documentation.
• Build observability into systems with logging, tracing, monitoring, alerting and cost tracking.
• Produce architecture artifacts including ADRs, blueprints, and dataflow diagrams to guide teams.
• Continuously learn, evaluate, and apply new AI design patterns and technologies.
• Collaborate with data engineers, ML engineers, and application developers to translate requirements into architecture decisions.

Education

Bachelor's Degree or equivalent

Tools

Anthropic certification

Job description

Job Description

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 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

Qualification

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, generative and classical AI/ML using at least one cloud vendor.

• Practical experience in the Agentic, LLM and 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

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