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AI Architect (Agentic AI)

kyndryl · Madrid

New
Senior 🇬🇧 English
LangGraph CrewAI AutoGen Google ADK LangChain LlamaIndex Azure AWS GCP Azure ML Vertex AI OpenShift AI Databricks MLflow Kubeflow Airflow MLOps

Job description

About the role

Kyndryl is looking for a senior AI Architect (AI Agentic) in Madrid to lead the design of Generative and Agentic AI architectures that drive efficiency and transformation across delivery teams. The role sits at the intersection of business, technology and innovation, acting as a technical authority and trusted advisor who helps Kyndryl scale AI adoption responsibly and sustainably.

Key responsibilities

  • Design and evolve end-to-end AI and Agentic AI architectures that enable innovation at enterprise scale.
  • Shape Kyndryl's strategy by turning objectives into coherent, high-impact architectures.
  • Ensure quality, scalability and security are embedded in every solution.
  • Guide multidisciplinary teams, from data scientists to engineers, toward architectural excellence and real business value.
  • Bridge business strategy and technology so initiatives align with innovation and operational goals.
  • Foster a culture of technical rigor, creativity and responsible AI.
  • Act as a technical reference and decision-maker, aligning stakeholders and guiding architectural choices.

Required profile

  • 7+ years of experience designing and delivering large-scale AI or ML architectures in complex enterprise environments.
  • Proven experience building and governing end-to-end AI systems, including production deployment.
  • Demonstrated leadership skills and the ability to influence cross-functional technical teams.
  • Bachelor's or Master's degree in Computer Engineering, Artificial Intelligence, Data Science or a related technical field.

Required skills

  • Deep knowledge of AI and ML architectures, cloud-native solutions and/or distributed systems.
  • Familiarity with multi-agent frameworks (LangGraph, CrewAI, AutoGen, Google ADK) and RAG architectures (LangChain, LlamaIndex).
  • Knowledge of at least one major cloud provider (Azure, AWS, GCP) and its AI platforms.
  • Solid understanding of MLOps frameworks (MLflow, Kubeflow, Airflow).
  • Preferred: exposure to edge AI or on-premise AI in regulated industries, strong presentation skills, advanced studies in AI, ML or Cloud Architecture, and cloud or MLOps certifications.

Questions fréquentes

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Source : ats:workday

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Published 4 hours ago

Expires 1 month from now

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kyndryl

Madrid