AI Large Language Model
Pubblicato il 21-10-2026 - Accenture in Assago
AI Large Language Model (LLM) Technology Architect
Career Level:
Associate Manager / Specialist
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