Data Scientist – Agentic AI

Job description

Who We Are 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. The Role Your RoleAs a Senior Data Scientist at Kyndryl, you will play a key role in designing, developing, and deploying advanced Artificial Intelligence solutions that address complex business challenges and deliver measurable value to our clients. You will work across the evolving AI landscape, combining traditional Machine Learning and advanced analytics with Generative AI, Large Language Models (LLMs), and emerging Agentic AI technologies to build intelligent, scalable, and reliable solutions. As part of a multidisciplinary team of AI specialists, data scientists, engineers, and architects, you will contribute to the full AI lifecycle, from problem definition and experimentation to production deployment, monitoring, and continuous improvement. This is an opportunity to work with cutting-edge technologies, contribute to innovative AI initiatives, and help organizations transform their operations through the responsible adoption of AI. What You Will DoShape AI-Driven TransformationDeliver business impact: Translate complex business needs into actionable AI and Data Science solutions, identifying opportunities where Machine Learning, Generative AI, and Agentic AI can create tangible value. Drive technical innovation: Explore and evaluate emerging AI methodologies, architectures, and technologies, combining established Data Science approaches with the latest advances in foundation models and intelligent agents. Connect technology and business: Work closely with clients and stakeholders to understand their challenges, define AI use cases, and communicate technical concepts, findings, and recommendations effectively. Design and Develop Advanced AI SolutionsDevelop Generative AI applications: Design and implement solutions leveraging LLMs, embedding models, Retrieval-Augmented Generation (RAG), prompt engineering, and model adaptation techniques to address enterprise use cases. Build Machine Learning solutions: Design, develop, train, and evaluate predictive and analytical models using statistical methods, supervised and unsupervised learning, deep learning, and other advanced Data Science techniques. Explore and implement Agentic AI: Develop intelligent agents and multi-agent systems capable of reasoning, planning, interacting with tools and enterprise systems, and executing complex workflows with appropriate human oversight. Apply rigorous evaluation: Define and implement evaluation methodologies to assess model accuracy, reliability, robustness, and business performance, including specific approaches for evaluating LLM-based and agentic systems. Build, Deploy, and ScaleTake solutions from concept to production: Lead the technical development of AI solutions, from exploratory analysis, prototyping, and experimentation to deployment and integration into enterprise environments. Apply MLOps and LLMOps practices: Collaborate with engineering and platform teams to establish reproducible, maintainable, and scalable AI solutions, incorporating versioning, automated testing, monitoring, and continuous improvement. Embrace AgentOps practices: Contribute to the evaluation, observability, and continuous improvement of agentic AI systems, monitoring agent behavior, tool interactions, reliability, and end-to-end process performance, while supporting responsible autonomy and human oversight. Work with modern AI technologies: Leverage Python, leading Machine Learning and Deep Learning frameworks, cloud AI platforms, and emerging GenAI and agentic frameworks, selecting the most appropriate technologies for each use case. Build responsible and trustworthy AI: Incorporate security, privacy, explainability, governance, and responsible AI principles throughout the solution lifecycle. Collaborate, Lead, and InspireCollaborate across disciplines: Work alongside AI architects, software engineers, data engineers, domain experts, and business stakeholders to design integrated, production-ready AI solutions. Provide technical leadership: Contribute to architectural and technical decisions, promote engineering best practices, and support the development of junior Data Scientists and AI practitioners. Foster innovation and knowledge sharing: Stay current with advances in AI research and industry practices, contributing to internal initiatives, technical communities, knowledge sharing, and the development of reusable AI assets. Engage with customers: Participate in technical discussions, workshops, demonstrations, and solution design activities, helping clients understand and adopt AI technologies effectively. Who You Are Required Skills and ExperienceEducation and professional experience: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Mathematics, Statistics, Engineering, or a related technical field, combined with solid professional experience in Data Science, Machine Learning, or AI development. Strong Data Science and Machine Learning foundations: Proven experience designing, developing, and evaluating Machine Learning models, with a solid understanding of statistical methods, supervised and unsupervised learning, and Deep Learning techniques. Generative AI and LLMs: Hands-on experience developing AI solutions leveraging Large Language Models, prompt engineering, embeddings, and Retrieval-Augmented Generation (RAG), with an understanding of their capabilities, limitations, and evaluation methodologies. Agentic AI development: Practical knowledge of AI agents and agentic architectures, including tool integration, orchestration, reasoning workflows, and the use of agentic development frameworks. Programming and AI frameworks: Strong proficiency in Python and relevant Data Science and AI libraries, such as Scikit-learn, PyTorch, TensorFlow, Hugging Face, or equivalent technologies. AI solution lifecycle: Experience across the AI development lifecycle, including data preparation, experimentation, model evaluation, deployment, and monitoring, with familiarity with MLOps, LLMOps, and emerging AgentOps practices. Analytical and problem-solving skills: Strong analytical thinking, the ability to translate business challenges into technical AI solutions, and a practical approach to experimentation and continuous improvement. Collaboration and communication: Ability to work independently and collaboratively within multidisciplinary teams, communicate technical concepts effectively, and engage with business and technical stakeholders. Preferred Skills and ExperienceAdvanced Agentic AI: Hands-on experience designing and implementing complex agentic workflows or multi-agent systems using frameworks such as LangGraph, AutoGen, CrewAI, or equivalent technologies. AgentOps and AI evaluation: Familiarity with agent observability, tracing, evaluation frameworks, LLM-as-a-Judge approaches, and reliability assessment of agentic systems, including tools such as Langfuse, LangSmith, RAGAS, or DeepEval. Advanced Generative AI: Experience with LLM fine-tuning, model optimization, advanced RAG architectures, or open-source foundation models. Cloud AI platforms: Experience with Azure AI, AWS, or Google Cloud AI/ML services. Relevant certifications are considered an advantage. Production AI engineering: Experience deploying AI applications in enterprise environments, including APIs, containerization, microservices, CI/CD pipelines, and scalable architectures. Responsible AI: Knowledge of AI governance, model explainability, security, privacy, and