Senior Lead, Data Science

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 RoleAs a Data Scientist at Kyndryl you are the bridge between business problems and innovative solutions, using a powerful blend of well-defined methodologies, statistics, mathematics, domain expertise, consulting, and software engineering. You'll wear many hats, and each day will present a new puzzle to solve, a new challenge to conquer.

We are seeking a highly skilled AI/ML Engineer with deep expertise in LLMs, agentic AI frameworks, MLOps, and Azure cloud environments. The ideal candidate will design, build, and deploy scalable AI systems leveraging modern LLM orchestration frameworks, vector search pipelines, and enterprise grade MLOps practices. This role also requires strong collaboration across squads, the ability to articulate AI solutions to stakeholders, and an understanding of regulated industries such as banking. As a Software Engineering - Developer at Kyndryl, you will be at the forefront of designing, developing, and implementing cutting-edge software solutions. Your work will play a critical role in our business offering, your code will deliver value to our customers faster than ever before, and your attention to detail and commitment to quality will be critical in ensuring the success of our products.

Key ResponsibilitiesArchitect, develop, and operationalize LLM based solutions using Azure OpenAI (GPT models, embeddings, vector search).Build agentic AI systems and multi agent orchestration using frameworks such as LangChain, LangGraph, or equivalent.

Implement RAG systems, retrieval pipelines, embeddings, and prompt engineering best practices.

Develop scalable AI/microservice architectures using Python, containers, APIs, and event driven systems.

Build and maintain MLOps pipelines including CI/CD for model deployment, versioning, monitoring, and telemetry.

Perform data engineering tasks including ETL, ingestion, transformation, and vectorization.

Deploy AI workloads securely on Azure cloud following enterprise security and governance standards.

Partner with business and tech stakeholders to translate business challenges into AI driven solutions.

Support model interpretability, explainability, and regulatory compliance for AI/ML systems.

The AI Engineer is responsible for designing, building, deploying, and optimizing AI powered solutions-including LLM based automation, multi agent systems, and intelligent workflows-to support banking, regulatory, operations, and back office modernization initiatives.

The engineer collaborates with data, platform, security, and application teams to operationalize AI models that are secure, compliant, scalable, and aligned with organisation's digital transformation roadmap.

Using design documentation and functional programming specifications, you will be responsible for implementing identified components. You will ensure that implemented components are appropriately documented, unit-tested, and ready for integration into the final product. You will have the opportunity to architect the solution, test the code, and deploy and build a CI/CD pipeline for it.

As a valued member of our team, you will provide work estimates for assigned development work, and guide features, functional objectives, or technologies being built for interested parties. Your contributions will have a significant impact on our products' success, and you will be part of a team that is passionate about innovation, creativity, and excellence. Above all else, you will have the freedom to drive innovation and take ownership of your work while honing your problem-solving, collaboration, and automation skills. Together, we can make a difference in the world of cloud-based managed services.

If you're ready to embrace the power of data to transform our business and embark on an epic data adventure, then join us at Kyndryl. Together, let's redefine what's possible and unleash your potential.

Your Future at KyndrylEvery position at Kyndryl offers a way forward to grow your career. We have opportunities that you won't find anywhere else, including hands-on experience, learning opportunities, and the chance to certify in all four major platforms. Whether you want to broaden your knowledge base or narrow your scope and specialize in a specific sector, you can find your opportunity here.

Who 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 Technical and Professional Experience8+ Years of experience with European regulatory reporting frameworks, including

EBA / ECB

reporting ,FINREP / COREP (high-level understanding) ,Basel III / Basel IV conceptsHands on experience with Azure OpenAI models & APIs (GPT, embeddings, vector search).Proficiency in LLMS, NLP, prompt engineering, embeddings, and RAG pipelines.

Experience with agentic AI and multi agent orchestration frameworks.

Strong working knowledge of LangChain, LangGraph, or similar orchestration tooling.

Strong Python programming skills for AI automation, pipelines, and backend servicesExperience with containerized deployments (Docker), microservices, and REST APIs.

Familiarity with event driven architectures (queues, pub/sub, message brokers).Hands on experience with MLOps toolchains (CI/CD for models, monitoring, telemetry).Data engineering proficiency in ETL, data ingestion, transformation, vectorization.

Experience working in Azure cloud ecosystems with secure deployment practices.

Preferred Technical and Professional ExperienceKnowledge of ML frameworks such as PyTorch or TensorFlow.

Experience with reinforcement learning, supervised and unsupervised ML techniques.

Familiarity with MLflow, feature stores, and enterprise model registriesExposure to banking operations-KYC, fraud, credit, payments.

Understanding of regulatory controls for AI (explain ability, model risk governance).Experience with GitLab CI/CD, Terraform/Helm, and Azure Kubernetes Service (AKS).Knowledge of observability platforms such as Dynatrace, Azure Monitor, or ELK.Being YouThe \