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Accenture

Advises businesses and runs their technology

AI Large Language Model Technology Architect

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeVienna, AustriaIn-person

About the Job

Requirements

  • Proven experience designing and deploying enterprise-grade advanced AI solutions using agentic, generative, and classical artificial intelligence and machine learning with at least one cloud vendor.
  • Practical experience in agentic, large language model, and generative artificial intelligence.
  • Coding proficiency in Python.
  • A solid foundation in architecting and operationalizing large language model-driven application architecture patterns.
  • Professional working experience coding engineering, machine learning, deep learning, and natural language processing solutions and applications.
  • Several years of hands-on industry experience as a machine learning architect designing big data, machine learning, and large-scale analytical engineering solutions.
  • A Bachelor's Degree or equivalent.

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, validating them through hands-on prototyping.
  • Evaluate design options and technical approaches and make 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 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 inform model selection and optimization.
  • Design and build the AI context layer, including context graph design, 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, hybrid, and reranking methods, into end-to-end retrieval-augmented generation 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.
  • Optimize 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, 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 that keeps 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 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.

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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