Senior AI Platform Architect for Enterprise Agentic AI

Accenture España; Madrid; Hybrid; EUR 110,000 - 140,000; Full time

Job summary

Kyndryl is seeking an AI Systems Architect to design an enterprise-scale Agentic AI platform that enables engineering teams to build, deploy, and operate production-ready AI agents in a secure, governed, and reusable manner.

You will collaborate with infrastructure teams on Kubernetes, GPU platforms, networking, and cloud-native AI infrastructure, and establish governance across policy enforcement, model lifecycle, and cost optimization.

Responsibilities

• Architect a reusable enterprise Agentic AI platform, including agent runtime, orchestration, context management, memory, evaluation, guardrails, and observability.
• Develop and maintain an Agent Harness that provides standardized runtime capabilities and Model Context Protocol (MCP) integration.
• Define "Golden Paths" that standardize AI agent development and deployment across engineering teams.
• Build a model-agnostic AI runtime supporting multiple LLM providers, AI gateways, and enterprise integrations.
• Collaborate with infrastructure teams on Kubernetes, GPU platforms, networking, and cloud-native AI infrastructure.
• Establish enterprise AI governance covering policy enforcement, security, prompt/model lifecycle management, human-in-the-loop workflows, evaluation, and cost optimization.
• Integrate AI agents with enterprise platforms such as CI/CD, ITSM, observability, cloud services, and business applications.
• Define engineering standards and mentor teams to drive enterprise adoption of Agentic AI best practices.

Job description

Kyndryl is seeking an AI Systems Architect to design an enterprise-scale Agentic AI platform that enables engineering teams to build, deploy, and operate production-ready AI agents in a secure, governed, and reusable manner.

You will collaborate with infrastructure teams on Kubernetes, GPU platforms, networking, and cloud-native AI infrastructure, and establish governance across policy enforcement, model lifecycle, and cost optimization.