AI & LLM Architect — Enterprise Solutions

Accenture UK & Ireland; West of England; Sur place; GBP 120 000 - 150 000; Plein temps

Résumé du poste

Accenture UK & Ireland is seeking a hands-on AI/LLM Architect to design and build advanced AI systems for enterprise use. This role is deeply technical and hands-on, spanning classical ML, generative AI, and agentic systems, with client engagements shaping real-world deployments.

You will translate requirements into architecture decisions, selecting patterns, benchmarking frameworks, and creating ADRs and design artifacts.

Qualifications

• Proven experience in designing & deploying enterprise grade advanced ai solutions using agentic, generative and classical AI/ML across cloud environments.
• 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, machine learning, deep learning and NLP solutions and applications.
• Several years of hands on experience as a machine learning architect in the industry designing big data, machine learning. large scale analytical engineering solutions.

Responsabilités

• 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

Connaissances

Python

LLM architecture

Generative AI

Agentic AI

Enterprise AI

Performance optimization

Formation

Bachelor's Degree

Description du poste

Accenture UK & Ireland is seeking a hands-on AI/LLM Architect to design and build advanced AI systems for enterprise use. This role is deeply technical and hands-on, spanning classical ML, generative AI, and agentic systems, with client engagements shaping real-world deployments.

You will translate requirements into architecture decisions, selecting patterns, benchmarking frameworks, and creating ADRs and design artifacts.