Data Scientist (FDE)

About the role

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; with proven expertise and a continuous flow of AI-powered insight, enabling smarter decisions, faster innovation, and a lasting competitive edge. For our people-Kyndryls-that means doing purposeful work that powers human progress. Join us and experience a flexible, supportive environment where your well-being is prioritized and your potential can thrive. As an Associate Data Scientist at Kyndryl's AI Innovation Hub, you'll be part of a team that turns data into intelligent, high-impact solutions. You'll collaborate with senior data scientists, ML engineers, and AI architects to design, train, and validate predictive and machine learning models that tackle real business and operational challenges. You'll participate in every stage of the model lifecycle - from data exploration and feature engineering to modeling, evaluation, and documentation - helping transform raw data into actionable insights. This is a hands-on, learning-focused role in which you'll work with modern technologies, contribute to scalable AI solutions, and grow your expertise within an environment that values experimentation, rigor, and curiosity. The Forward Deployed Engineer (FDE) is a delivery-first role for early career engineers who want to work where AI systems meet real enterprise complexity. As an FDE, you will embed directly with customer delivery teams to help build, prototype, and deploy AI-enabled solutions in production environments. This is not a traditional software engineering role and not classic consulting. It is a hybrid role designed for real-world AI deployment, where success is measured by outcomes and production scaled solutions, not experiments. You will deliver defined technical components under guidance from senior engineers, while developing consulting fundamentals through direct customer exposure. You will work with modern AI development tools, generative and agentic AI architectures, and enterprise cloud platforms, learning how to translate technical work into customer value.

Responsibilities

• Customer Engagement & Solution Integrity: 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
• Rapid Prototyping: Build and iterate custom AI solutions tailored to customer needs, leveraging agentic AI frameworks
• Ownership, Deployment & Partnership: Own delivery end to end, from scoping to production. Work as part of the customer team to engineer and deploy production‑ready solutions that drive adoption and measurable business outcomes.
• Optimization: Troubleshoot and enhance system performance, scalability, and reliability
• Field Intelligence: Systematically capture deployment learnings, document best practices, and proactively share insights to inform enhancements to Kyndryl's core platforms and frameworks
• Continuous Learning: Adapt quickly to emerging technologies and evolving customer requirements by engaging in ongoing professional development
• Platform Contribution: Actively contribute to the evolution of Kyndryl's AI platforms through feedback, code contributions, and collaboration with product teams

Requirements

• Required skills and experience
• 2-4 years of experience in data science, advanced analytics, or machine learning projects.
• Programming Proficiency: Demonstrable expertise in Python, (or another modern language such as C#, Node.js or TypeScript); experience working with AI/ML frameworks like TensorFlow or PyTorch is a plus
• Software Engineering: Solid grasp of the software delivery lifecycle, version control (Git & GitHub), and data engineering tools such as Pandas and Spark
• Cloud & Distributed Systems: Experience with cloud AI platforms (AWS, Azure, Google AI) and distributed computing architectures
• . Located in Spain and Fluent in Spanish.
• Preferred skills and experience
• Open-Source Ecosystems: Familiarity with community-driven AI tools and libraries, including Hugging Face and relevant repositories
• T-shaped Profile: Deep technical expertise in one or two domains, with broad understanding across AI/ML, cloud, and consulting
• Agentic AI Systems: 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
• Retrieval-Augmented Generation (RAG) & Knowledge Systems: Practical knowledge of deploying RAG architectures and integrating structured and unstructured knowledge sources into AI solutions
• Performance & Security: Knowledge of system-level optimisation and security best practices for scalable AI systems
• Business Acumen: Ability to translate business requirements into technical solutions

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