AI Architect

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

Job Description

As a AI Architect at Kyndryl, you'll play a key role in designing, refining, and implementing the architectures behind next-generation AI and Agentic AI solutions.

You'll work at the intersection of innovation and engineering, translating strategic business objectives into tangible, enterprise-ready architectures that are scalable, secure, reliable, and maintainable.

You'll collaborate with architects, data scientists, ML engineers, software engineers, cloud specialists, security teams, and business stakeholders to design systems that combine technological excellence with measurable business value - systems that are not only intelligent, but also trustworthy, traceable, governable, and sustainable.

• Design and implement end-to-end AI and Agentic AI architectures that align business objectives with technical excellence.
• Transform functional, analytical, and non-functional requirements into efficient, modular, scalable, and maintainable system designs.
• Define architecture patterns covering areas such as AI agents, foundation models, data platforms, APIs, integration layers, orchestration, observability, security, and cloud infrastructure.
• Collaborate with cross-functional teams to ensure solutions meet high standards of scalability, performance, resilience, security, and reliability.
• Contribute to the creation of reference architectures, reusable components, design patterns, and technical standards that accelerate the delivery of AI solutions across the organization.
• Act as a trusted technical partner throughout the solution lifecycle, contributing to architecture and design reviews and providing guidance during implementation.
• Evaluate emerging technologies across the AI, Agentic AI, data, automation, and cloud ecosystems, identifying opportunities that can deliver tangible business value.
• Ensure AI solutions integrate effectively with existing enterprise systems, data platforms, applications, APIs, and cloud environments.
• Champion responsible AI and secure-by-design principles, embedding governance, traceability, privacy, security, and compliance into the architecture.
• Promote sustainable engineering practices, considering cost efficiency, infrastructure utilization, model efficiency, operational complexity, and long-term maintainability when making architectural decisions.
• Help bridge the gap between experimentation and production, ensuring AI concepts can evolve into robust, enterprise-grade solutions.

• 3-6 years of experience in designing and implementing AI/ML solutions or advanced analytics architectures.
• Proven experience integrating AI models into production environments (APIs, microservices, or data pipelines).
• Solid understanding of AI and ML architectures, including model lifecycle (data ingestion, training, validation, deployment).
• Proficiency with cloud ecosystems (Azure, AWS, GCP) and their AI/ML services (e.g., Azure ML, Vertex AI, OpenShift AI).
• Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow) and container orchestration (Kubernetes, Docker).
• Familiarity with agentic AI frameworks (LangChain, CrewAI, AutoGen, LlamaIndex) and RAG architectures.
• Working knowledge of vector databases, APIs, and integration patterns for intelligent systems.
• Understanding of data privacy and regulatory frameworks (GDPR, EU AI Act, Responsible AI principles).

• Bachelor's or Master's degree in Computer Engineering, Artificial Intelligence, Data Science, or related field.
• Postgraduate studies (Master's or PhD) in AI, Big Data, or Cloud Computing are highly valued.
• Certifications in cloud architecture (Azure, AWS, or GCP) and MLOps frameworks are a plus.
• Continuous learning mindset and strong curiosity about emerging AI paradigms and technologies.

• Experience designing modular, scalable, and maintainable architectures for AI-driven solutions.
• Knowledge of hybrid or federated AI deployments, integrating enterprise data and large language models.
• Exposure to observability and monitoring practices in AI workloads.
• Familiarity with semantic search, knowledge graphs, or LLM optimization pipelines.
• Experience collaborating in agile or DevOps environments, bridging data science and engineering teams.
• Ability to document and communicate architecture decisions clearly, supporting alignment across stakeholders.
• Curiosity to experiment with new frameworks and evaluate their maturity for enterprise adoption.