Senior Data Scientist - Agentic AI

About the role

Kyndryl runs and reimagines the mission-critical technology systems behind the world's leading businesses. As a Senior Data Scientist, you will design, develop and deploy advanced AI solutions that combine Machine Learning, Generative AI, Large Language Models and emerging Agentic AI technologies. You will work in a multidisciplinary team of AI specialists, data scientists, engineers and architects, covering the full AI lifecycle from problem definition and experimentation to production deployment, monitoring and continuous improvement.

Key responsibilities

• Translate complex business needs into AI and data science solutions, working with clients and stakeholders to define use cases and communicate results.
• Design and implement Generative AI applications using LLMs, embeddings, RAG and prompt engineering.
• Build machine learning solutions, including predictive and analytical models with supervised and unsupervised learning and deep learning.
• Develop intelligent agents and multi-agent systems that reason, plan, interact with tools and enterprise systems, and evaluate them rigorously.
• Move solutions from prototype to production, applying MLOps, LLMOps and AgentOps practices, including observability and monitoring of agent behaviour.
• Incorporate security, privacy, explainability and governance throughout the lifecycle.
• Work with AI architects, engineers and domain experts, and share knowledge internally and with customers.

Required profile

• Bachelor's or Master's degree in Computer Science, AI, Mathematics, Statistics, Engineering or a related field, with solid professional experience in data science, machine learning or AI.
• Proven experience designing, developing and evaluating ML models, and hands-on experience with LLMs, prompt engineering, embeddings and RAG.
• Practical knowledge of AI agents, tool integration, orchestration and agentic frameworks.
• Experience across the AI lifecycle, including data preparation, experimentation, evaluation, deployment and monitoring.
• Preferred: experience with LangGraph, AutoGen, CrewAI, agent observability tools (Langfuse, LangSmith, RAGAS, DeepEval), LLM fine-tuning, Azure/AWS/Google Cloud AI, and deploying AI applications in enterprise environments.

Required skills

• Python and data science/AI libraries such as Scikit-learn, PyTorch, TensorFlow, Hugging Face or equivalents.
• MLOps, LLMOps and AgentOps practices.
• Preferred: cloud AI platforms, API deployment, containerization, microservices, CI/CD pipelines, and responsible AI principles.

The company offers a hybrid-friendly culture, career development with learning opportunities and certifications, and welcomes candidates who do not meet every requirement.