AI Large Language Model (LLM) Technology Architect (All Genders)

Accenture DACH; Würzburg; Vor Ort; EUR 110.000 - 150.000; Vollzeit

Zusammenfassung

Accenture DACH in Würzburg seeks an AI Large Language Model (LLM) Technology Architect to lead hands-on design and delivery of advanced AI systems. You will architect agent-based solutions, integrate foundation models, and build scalable, secure architectures across client engagements, collaborating with data engineers, ML engineers, and application developers.

The role emphasizes crafting ADRs, data flows, and integration specifications, while ensuring performance, observability, and

Qualifikationen

• Proven experience in designing & deploying enterprise grade AI solutions using agentic, generative and classical AI/ML
• Experience in Agentic, LLM and Generative AI space
• Proficient in Python
• Solid foundation in architecting and operationalizing LLM-driven application architecture patterns
• Professional experience in coding engineering, ML, DL and NLP solutions
• Several years as a machine learning architect designing big data, ML and large-scale analytical solutions

Aufgaben

• Independently design, build, and deliver software components across the AI architecture - end-to-end from design to testing
• Design and build AI agent architectures including prompts, tools, and memory systems
• Design and implement agent orchestration patterns with state management and error recovery
• Evaluate design options balancing capability, cost, performance, and reliability
• Design and run evaluation strategies measuring accuracy, relevance, and faithfulness
• Architect and implement foundation model integrations and customization approaches
• Design and build model adaptation and fine-tuning pipelines
• Build embedding, vector storage, and retrieval into end-to-end RAG pipelines
• Design context layer and memory components for grounded outputs
• Create reusable components and templates across engagements
• Optimize cost and performance, caching, and resource utilization
• Implement guardrails, PII handling, and access controls for security
• Build governance with versioning, audit trails, and lineage
• Incorporate observability with logging, tracing, and monitoring
• Produce architecture artifacts to guide broader teams
• Continuously learn and apply new AI design patterns
• Collaborate with data engineers, ML engineers, and developers to translate requirements

Kenntnisse

Agentic AI

Generative AI

LLM Architect

Python

ML/NLP

Big Data ML

Ausbildung

Bachelor's Degree or equivalent

Jobbeschreibung

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 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.