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.

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The Role
What You Will Do

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- 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

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- 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

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- 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

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- 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. xqbhyrx

- Platform engineering in regulated environments (finance, insurance, public sector).

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