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Accenture

Advises businesses and runs their technology

AI LLM Architect - Engineering Services

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeBerlin, GermanyNo salary listed

About the Job

Requirements

  • A Bachelor's degree or equivalent is required.
  • Proven experience designing and deploying AI or machine learning solutions using at least one cloud vendor as an AI/ML architect is required.
  • Industry experience within Engineering Services is required.
  • Experience in agentic AI, large language models, and generative AI is required; manufacturing and operations experience is preferred.
  • Professional experience architecting and operationalizing large-language-model-driven application architecture patterns is required.
  • Practical experience in coding and engineering, machine learning, deep learning, and natural language processing solutions and applications is required.
  • Coding experience using Python is required.
  • German proficiency at B2 level is required.

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, prompts, tools, skills, multi-agent orchestration, and memory systems, making deliberate design-pattern and technology choices.
  • Design and implement agent orchestration patterns for task handoffs, communication, state management, and error recovery, and validate them through hands-on prototyping.
  • Evaluate design options and technical approaches, making justified choices that balance capability, cost efficiency, performance, and enterprise-grade reliability.
  • Design, build, and run evaluation strategies and harnesses to measure agent and system quality using metrics such as accuracy, relevance, and faithfulness, and translate findings into design improvements.
  • Architect and implement foundation-model integrations by selecting models, invocation patterns, and customization approaches, including fine-tuning, retrieval-augmented generation, and 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 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 retrieval components that ground AI outputs in client knowledge.
  • Build embedding, vector-storage, and retrieval capabilities, including semantic retrieval, hybrid retrieval, and reranking, into end-to-end retrieval-augmented-generation pipelines, using 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.
  • Optimize model usage, inference patterns, caching, and resource utilization to meet target latency, throughput, cost, and performance objectives.
  • Design, build, and validate systems against enterprise non-functional requirements, implementing guardrails, prompt-injection defenses, personally identifiable information handling, and access controls for security and Responsible AI.
  • Build governance controls, including versioning, audit logging, and lineage tracking, and produce model documentation to keep systems auditable.
  • Build observability into systems through 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, architecture blueprints, design documents, agent-orchestration and integration-pattern specifications, component diagrams, and data-flow diagrams, to guide and enable broader engineering teams.
  • Continuously learn, evaluate, and apply new design patterns, frameworks, and technologies across the evolving AI landscape while balancing innovation with enterprise-grade reliability.
  • Collaborate with cross-functional delivery teams, including data engineers, machine-learning engineers, and application developers, to translate requirements into architecture decisions that meet stakeholder needs.

Desired Qualifications

  • Experience in the agentic AI, large language model, and generative AI space within the manufacturing and operations industry is preferred.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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