At Kyndryl, we run and reimagine the mission‑critical technology systems that drive advantage for the world’s leading businesses. We are at the heart of progress; we use proven expertise and a continuous flow of AI‑powered insight to enable smarter decisions, faster innovation, and a lasting competitive edge.
The Role
What You Will Do

Design and build the agent harness: runtime, orchestration, tool and MCP integration, context and memory, evaluation, guardrails and observability.
Build golden paths and reusable patterns so teams ship agents that are reliable, governed and measurable, instead of one‑off scripts.
Define the oversight model: policy‑as‑code, human‑in‑the‑loop checkpoints, loop and failure detection, evaluation harnesses, and cost control.
Integrate agents with the systems they act on: CI/CD, infrastructure, cloud services, observability and ticketing, through APIs and MCP.
Make agentic productivity measurable: define what productive means for each use case, instrument it, and run the harness on that data.
Keep the harness model‑agnostic and versioned, so it survives model upgrades and provider changes without rewrites.
Mentor teams, run enablement, and reduce friction between Dev, Ops, Security and the people adopting agents.

What Success Looks Like In Your First Year

Teams ship agents through the harness golden paths instead of bespoke scripts.
Agentic workflows run in production with guardrails,



evaluation and measured productivity impact.
The harness absorbs at least one model or provider upgrade without a rewrite.

Who You Are
What you will bring

5+ years in Platform Engineering, DevOps or SRE, building developer platforms or runtime and orchestration systems in production.
Hands‑on experience building agent harnesses or agentic systems: runtime, tool and MCP integration, orchestration, evaluation and guardrails.
Solid software engineering, primarily in Python, with the depth to build production systems and abstractions, not only prompts.
Strong cloud‑native background: Kubernetes, containers, Infrastructure as Code, and one major cloud (AWS, Azure or GCP).
A working grasp of how LLM agents fail and the engineering that makes them reliable: context design, loop and failure detection, verification and evaluation.
Experience treating a platform as a product: contracts, SLOs, adoption metrics and real users.
Professional working English and Spanish.

Nice to have

Agent frameworks such as LangGraph, CrewAI, AutoGen etc. and the Model Context Protocol.
Serious production use of AI‑assisted development tools (Claude Code, Copilot, Codex).
LLMOps, agent evaluation, or observability for agentic systems.
Prior work on harnesses that stay stable across model upgrades.
Platform engineering in regulated environments (finance, insurance, public sector).

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📌 Senior Platform Engineer – Agentic AI & Harness Engineering (Madrid)
🏢 Kyndryl
📍 Madrid