A

Accenture

Advises businesses and runs their technology

AI Large Language Model – Technology Lead/Principal Architect

AI Application EngineeringPosted on Nov 11
Full-timeLondon, UKNo salary listedIn-person

About the Job

Requirements

  • A Bachelor's degree or equivalent.
  • Extensive experience designing and deploying enterprise-grade advanced AI solutions using agentic, generative, and classical AI or machine learning with at least one cloud vendor.
  • Proven experience in the large language model and generative AI space.
  • Expertise architecting and operationalizing large-language-model-driven application architecture patterns.
  • Deep experience engineering machine learning, deep learning, and natural language processing solutions and applications.
  • Several years of hands-on industry experience as a machine architect 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 enterprise AI implementation roadmaps that sequence investments for lasting competitive advantage.
  • Own the complete end-to-end technical solution for complex AI platforms, ensuring every domain is cohesively designed and aligned to business objectives and enterprise standards.
  • Translate governing architecture principles into a concrete, defensible technical solution for domain teams to build against.
  • Build innovative prototypes and proofs of concept hands-on using emerging technologies to de-risk decisions and prove value early.
  • Perform technology assessments and comparisons, making evidence-based recommendations on tools, frameworks, and platforms.
  • Set the architectural direction for model- and tool-agnostic multi-agent ecosystems, including orchestration, memory, and tool or skill use governed through a registry-bound AI Gateway.
  • Establish the agent registry and certification model that prevents uncertified agents from reaching production.
  • Define memory as a first-class abstracted platform service decoupled from any underlying vendor engine.
  • Define the foundation model and inference strategy, including adaptation, fine-tuning, and dynamic cost-, quality-, and latency-aware routing.
  • Set standards for high-throughput, low-latency inferencing and classical machine learning deployment within unified, production-ready platforms.
  • Own the architecture of the enterprise context layer, including knowledge graphs, ontologies, vector search, and semantic retrieval, grounding the solution in client knowledge.
  • Set the 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 and consistently across every domain.
  • Establish the identity and authorization model, including per-agent identity, IAM/IAP binding, and defense-in-depth enforcement.
  • Define a layered guardrail framework applied at every boundary, balancing protection with performance.
  • Govern the MCP control plane, including registry, gateway, and risk scoring, across internal and third-party servers.
  • Mandate adoption of productized evaluation and observability stacks over building them internally.
  • Establish FinOps as a first-class concern, including usage labelling, gateway-enforced budgets, and cost-per-archetype as a planning input.
  • Make definitive 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 the practice's reusable reference architectures, frameworks, and assets, with a deliberate adopt-over-build stance.
  • Conduct deep-dive architecture workshops and working sessions with client executives and engineering teams.
  • Produce and govern authoritative architecture artifacts, including blueprints, reference architectures, architecture decision records, and integration specifications, that guide delivery at scale.
  • Serve as a recognized AI thought leader, shaping the practice's point of view and representing 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

Recently Posted Jobs

Explore more opportunities at Accenture

See more jobs