As a Forward Deployed Engineer (FDE) at Kyndryl, you own end-to-end delivery for complex AI engagements, translating business challenges into technical solutions.

Responsibilities

- Partner with customers to understand business challenges and translate them into high‑quality, fit‑for‑purpose technical solutions, prioritising the right outcome over the fastest one.
- Build and iterate custom AI solutions tailored to customer needs, leveraging agentic AI frameworks.
- Own delivery end to end, from scoping to production, working as part of the customer team to engineer and deploy production‑ready solutions that drive adoption and measurable business outcomes.
- Diagnose and enhance system performance, scalability, and reliability.
- Capture deployment learnings, document best practices, and share insights to improve Kyndryl’s core platforms and frameworks.
- Adapt quickly to emerging technologies and evolving customer requirements by engaging in ongoing professional development.
- Contribute to the evolution of Kyndryl’s AI platforms through feedback, code contributions, and collaboration with product teams.

Required Skills and Experience
- Demonstrable expertise in Python (or another modern language such as C#, Node.Js or TypeScript);
experience with AI/ML frameworks like TensorFlow or PyTorch is a plus.
- Solid grasp of the software delivery lifecycle, version control (Git & GitHub),



and data engineering tools such as Pandas and Spark.
- Experience with cloud AI platforms (AWS, Azure, Google AI) and distributed computing architectures.

Preferred Skills and Experience
- Familiarity with community-driven AI tools and libraries, including Hugging Face and relevant repositories.
- Deep technical expertise in one or two domains, with broad understanding across AI/ML, cloud, and consulting.
- Experience designing, building, or integrating multi‑agent systems and orchestration frameworks (e.G., LangGraph, Semantic Kernel, Agent Framework, AutoGen, CrewAI), including the development of agent protocols and coordination mechanisms.
- Practical knowledge of deploying RAG architectures and integrating structured and unstructured knowledge sources into AI solutions.
- Knowledge of system‑level optimisation and security best practices for scalable AI systems.
- Ability to translate business requirements into technical solutions and communicate technical value to diverse stakeholders, including executive audiences.
- Willingness to travel and work on customer premises as required. xhfqzwm

Education
- Degree‑level qualification in Computer Science, Informatics, Data Science, Engineering, or a closely related discipline — or equivalent professional experience.

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📌 Lead Agentic & Ai Architect (Fde) At Kyndryl (Madrid)
🏢 Kyndryl
📍 Madrid