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Capgemini

Global consulting, technology services, outsourcing

AI Architect

AI Application EngineeringUpdated on Nov 14
Full-timeDubai - United Arab EmiratesNo salary listedIn-person

About the Job

Requirements

  • Proven experience as an AI Architect or senior technical architect, with 8–12 years of hands-on experience across Generative AI, Large Language Models, retrieval-augmented generation and Agentic AI, and practical application of IAF, TOGAF or an equivalent recognised architecture framework.
  • Strong command of AI engineering fundamentals, including Python, REST APIs, vector databases, GPU and inference concepts, containerisation and cloud AI platforms, with experience governing MLOps and LLMOps pipelines end-to-end.
  • Demonstrated ability to define and operationalise AI evaluation strategies, including Golden Dataset design, model benchmarking, drift detection and observability, balancing AI autonomy with deterministic controls and human accountability.
  • Experience architecting AI and business-process orchestration solutions using tools such as Langflow, LangChain, LangGraph or Flowable, with solid understanding of BPMN 2.0 and the ability to translate business processes into governed, scalable AI-integrated workflows.
  • Experience in security and Responsible AI by design, including secure model endpoints, guardrails, controlled tool access and appropriate explainability practices.

Responsibilities

  • Define target architectures for Large Language Models, retrieval-augmented generation, Agentic AI, Computer Vision and multimodal capabilities, including agent design, memory, function calling, human-in-the-loop controls and multi-agent patterns.
  • Architect and implement AI and business-process orchestration solutions using platforms such as Langflow and Flowable, translating complex business processes into executable workflows that integrate people, AI agents, APIs and enterprise systems using BPMN 2.0.
  • Establish and govern the AI evaluation strategy, including Golden Dataset methodology, task-specific metrics, benchmarking, regression evaluation and maintaining clear separation between training and evaluation data.
  • Define production observability frameworks covering drift detection, quality degradation monitoring, feedback loops and continuous re-evaluation of approved AI solutions.
  • Design local and sovereign model architectures using Ollama, vLLM and the Hugging Face ecosystem, evaluating quality, security, sovereignty, cost and operational trade-offs between local and managed models.
  • Define retrieval-augmented generation pipelines including ingestion, chunking, embedding, vector search, hybrid retrieval and reranking, as well as Computer Vision pipelines for classification, detection, segmentation and anomaly detection.
  • Govern the full MLOps and LLMOps lifecycle, from development and evaluation through deployment, observation and continuous improvement, while embedding security, Responsible AI principles and architectural governance throughout.
  • Act as the AI architecture authority within the solution, align with the Lead Solution Architect, guide AI/ML and Computer Vision engineers, and communicate with non-technical stakeholders.

About the Company

Capgemini

Global consulting, technology services, outsourcing

Capgemini provides consulting, technology services, and outsourcing to help organizations transform digitally. Its offerings include strategy and transformation consulting, system integration, application development, IT infrastructure management, and managed IT operations. It differentiates itself with a global presence in over 50 countries and a focus on applying AI, cloud, and cybersecurity to real business problems. Its goal is to help clients navigate digital transformation and improve efficiency and innovation through tailored, contract-based or managed services.

Company size
10,001+
Company stage
IPO
Headquarters
Paris, France
Founded
1967

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