Cybersecurity AI/LLM Architect — Hands-On Enterprise

Accenture PLC; Brussel Hoofdstad; On-site; EUR 90,000 - 130,000; Full time

Job summary

Accenture is seeking an AI/LLM Architect in Belgium to design and build enterprise AI systems, focusing on agentic architectures, retrieval‑augmented generation, and grounding outputs in enterprise data. You will work hands‑on across model integration, memory systems, and non‑functional requirements.

Collaborate with data engineers and developers, produce ADRs and architecture diagrams, and ensure security, governance, and observability while delivering scalable AI solutions for client

Qualifications

• Proven experience in designing & deploying AI/ML solutions using at least one cloud vendor as an AI/ML architect.
• Experience in the Agentic, LLM and Generative AI space.
• Professional experience architecting and operationalizing LLM driven application architecture patterns.
• Strong knowledge of coding and engineering, machine learning, deep learning and NLP solutions and applications. Minimum of 2 years of coding experience using python
• Experience in the Cyber Security industry.

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 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

Skills

AI/ML architecture

LLM Generative AI

Python programming

Cybersecurity experience

Cloud platforms

Education

Bachelor’s degree

Tools

Cloud platforms

Job description

Accenture is seeking an AI/LLM Architect in Belgium to design and build enterprise AI systems, focusing on agentic architectures, retrieval‑augmented generation, and grounding outputs in enterprise data. You will work hands‑on across model integration, memory systems, and non‑functional requirements.

Collaborate with data engineers and developers, produce ADRs and architecture diagrams, and ensure security, governance, and observability while delivering scalable AI solutions for client