AI Architect

Stack

Responsibilities

• ▹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 AI agents, foundation models, data platforms, APIs, integration layers, orchestration, observability, security and cloud infrastructure
• ▹Collaborate with cross-functional teams to ensure high standards of scalability, performance, resilience, security and reliability
• ▹Contribute to reference architectures, reusable components, design patterns and technical standards that accelerate delivery of AI solutions across the organization
• ▹Act as a trusted technical partner throughout the solution lifecycle, contributing to architecture and design reviews and guiding implementation
• ▹Evaluate emerging technologies across the AI, Agentic AI, data, automation and cloud ecosystems, identifying opportunities with 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
• ▹Help bridge the gap between experimentation and production so AI concepts evolve into robust, enterprise-grade solutions

Requirements

• ▹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 a related field

Nice to have

• ▹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
• ▹Postgraduate studies (Master's or PhD) in AI, Big Data or Cloud Computing
• ▹Certifications in cloud architecture (Azure, AWS or GCP) and MLOps frameworks
• ▹Curiosity to experiment with new frameworks and evaluate their maturity for enterprise adoption

Soft skills

What we offer

• ▹Flexible, hybrid-friendly culture
• ▹Be Well programs supporting financial, mental, physical and social health
• ▹Personalized development goals and continuous feedback
• ▹Learning opportunities, including certifications with Microsoft, Google and Amazon, coaching and hands-on experiences

About the company

Kyndryl runs and reimagines the mission-critical technology systems of the world's leading businesses.