Forward Deployed Engineer

Who We AreAt 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.

The RoleThe 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.

What You Will DoCustomer 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 oneRapid Prototyping: Build and iterate custom AI solutions tailored to customer needs, leveraging agentic AI frameworksOwnership, 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 reliabilityField

Intelligence: Systematically capture deployment learnings, document best practices, and proactively share insights to inform enhancements to Kyndryl's core platforms and frameworksContinuous

Learning: Adapt quickly to emerging technologies and evolving customer requirements by engaging in ongoing professional developmentPlatform

Contribution: Actively contribute to the evolution of Kyndryl's AI platforms through feedback, code contributions, and collaboration with product teamsWho You AreYou're good at what you do and possess the required experience to prove it. However, equally as important – you have a growth mindset; keen to drive your own personal and professional development. You are customer‐focused – someone who prioritizes customer success in their work. And finally, you're open and borderless – naturally inclusive in how you work with others.

Required Skills

And ExperienceProgramming 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 plusSoftware

Engineering: Solid grasp of the software delivery lifecycle, version control (Git & GitHub), and data engineering tools such as Pandas and SparkCloud & Distributed Systems: Experience with cloud AI platforms (AWS, Azure, Google AI) and distributed computing architecturesPreferred Skills And ExperienceOpen‐Source Ecosystems: Familiarity with community‐driven AI tools and libraries, including Hugging Face and relevant repositoriesT‐shaped

Profile: Deep technical expertise in one or two domains, with broad understanding across AI/ML, cloud, and consultingAgentic

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 mechanismsRetrieval‐Augmented Generation (RAG) & Knowledge Systems: Practical knowledge of deploying RAG architectures and integrating structured and unstructured knowledge sources into AI solutionsPerformance & Security: Knowledge of system‐level optimisation and security best practices for scalable AI systemsBusiness

Acumen: Ability to translate business requirements into technical solutions and communicate technical value to diverse stakeholders, including executive audiencesFlexibility: Willingness to travel and work on customer premises as requiredEducation: Degree‐level qualification in Computer Science, Informatics, Data Science, Engineering, or a closely related discipline -