AI Large Language Model (LLM) Technology Architect

Accenture Belgium; Brussel; On-site; EUR 142,000 - 177,000; Full time

Job summary

Accenture Belgium seeks an hands-on AI/LLM Architect to design and build enterprise AI systems in client engagements in London. You will lead architecture decisions across ML, generative AI, and agentic systems, delivering end-to-end software components.

You'll prototype agent orchestration, memory, RAG integration, and grounding; ensure security, observability, and governance; produce ADRs and diagrams, collaborating with data engineers and application developers to scale AI solutions.

Qualifications

• Proven experience designing & deploying enterprise-grade AI solutions using agentic, generative and classical AI/ML with at least one cloud vendor.
• Practical experience in 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, ML, DL and NLP solutions and applications.
• Several years of hands-on experience as a machine learning architect in big data and large-scale analytical engineering solutions.

Responsibilities

• Design, build, and deliver AI software components end to end as a hands-on practitioner.
• Architect AI agent systems, agents prompts, tools, skills, and memory.
• Create agent orchestration patterns for task handoffs, state management and error recovery.
• Evaluate design options balancing capability, cost, performance, and reliability.
• Develop evaluation strategies for agent and system quality and translate findings into improvements.
• Architect foundation model integrations with fine-tuning, RAG, and custom integration.
• Design model adaptation and fine-tuning pipelines using transformer architectures.
• Build the AI context layer with context graphs and ingestion pipelines.
• Integrate embedding, vector storage, and retrieval into RAG pipelines.
• Design context assembly and memory components for grounded outputs.
• Identify reusable components and templated patterns for engagements.
• Design for cost efficiency and performance, including caching and resource use.
• Build governance controls, versioning, audit logs, and model documentation.
• Implement observability: logging, tracing, monitoring, alerts, cost tracking.
• Produce architecture artifacts to guide broader engineering teams.
• Continuously learn and apply new patterns and technologies.
• Collaborate with cross-functional teams to translate requirements into architecture.

Skills

Agentic AI

LLM & Generative AI

Python

Enterprise AI architecture

ML & NLP

Education

Bachelor's Degree or equivalent

Tools

Cloud vendor

Job description

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 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

EDUCATION

• Bachelor's Degree or equivalent

Basic (required) Qualification

• 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 big data, machine learning. large scale analytical engineering solutions.