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Accenture

Advises businesses and runs their technology

AI LLM Technology Architecture Senior Manager

AI & Machine LearningConfirmed live in the last 24 hours
Full-timeKronberg, GermanyNo salary listedIn-person

About the Job

Requirements

  • Bachelor's degree or equivalent.
  • Significant experience designing and deploying enterprise-grade advanced AI solutions using agentic, generative, and classical AI/ML with at least one cloud vendor.
  • Working experience in the large language model and generative AI space.
  • Experience architecting and operationalizing large language model-driven application architecture patterns.
  • Considerable experience in engineering, machine learning, deep learning, and natural language processing solutions and applications.
  • Professional experience as a machine architect in the industry designing big data, machine learning, and large-scale analytical engineering solutions.

Responsibilities

  • Partner with CIOs, CTOs, and business leaders to shape enterprise AI strategy, connecting business goals to a coherent, sequenced technical vision.
  • Lead enterprise AI assessments and build implementation roadmaps that sequence investments for lasting competitive advantage.
  • Own end-to-end technical solutions for complex AI platforms, ensuring each domain is cohesively designed and aligned to business objectives and enterprise standards.
  • Translate governing architecture principles into concrete, defensible technical solutions for domain teams.
  • Build hands-on prototypes and proofs of concept using emerging technologies to de-risk decisions and demonstrate value early.
  • Assess technologies and make evidence-based recommendations on tools, frameworks, and platforms.
  • Set architectural direction for model- and tool-agnostic multi-agent ecosystems, including orchestration, memory, and tool and skill use governed through a registry-bound AI gateway.
  • Establish the agent registry and certification model to prevent uncertified agents from reaching production.
  • Define memory as an abstracted platform service decoupled from underlying vendor engines.
  • Define foundation model and inference strategies, including adaptation, fine-tuning, and dynamic cost-, quality-, and latency-aware routing.
  • Set standards for high-throughput, low-latency inference and classical machine learning deployment in unified, production-ready platforms.
  • Own enterprise context-layer architecture, including knowledge graphs, ontologies, vector search, and semantic retrieval.
  • Set design direction for context assembly and memory that manages prompts, context windows, and conversational state across the platform.
  • Ensure security, governance, observability, performance, and scalability are addressed holistically across every domain.
  • Establish identity and authorization models, including per-agent identity, identity and access management and identity-aware proxy binding, and defense-in-depth enforcement.
  • Define a layered guardrail framework at every boundary, balancing protection with performance.
  • Govern the Model Context Protocol control plane, including registry, gateway, and risk scoring across internal and third-party servers.
  • Mandate adoption of productized evaluation and observability stacks rather than building them internally.
  • Establish FinOps practices including usage labeling, gateway-enforced budgets, and cost per archetype as a planning input.
  • Make decisions on design patterns, reference architectures, frameworks, and technology selections, balancing innovation with pragmatism.
  • Lead and integrate the work of domain architects and specialists, resolving cross-domain tensions into a unified, enterprise-ready system.
  • Build reusable practice reference architectures, frameworks, and assets, with an adopt-over-build approach.
  • Conduct deep-dive architecture workshops and working sessions with client executives and engineering teams.
  • Produce and govern architecture blueprints, reference architectures, architecture decision records, and integration specifications to guide delivery at scale.
  • Shape the practice's AI point of view and represent the firm externally through publications and conference engagements.

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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