Bachelor's degree or equivalent practical experience.
10 years of experience with cloud native architecture in a customer-facing or support role.
Experience with practical application and architectural considerations of AI/ML, including Machine Learning (ML) lifecycle, ML frameworks (e.g., TensorFlow, PyTorch) and MLOps principles.
Experience in developing data warehouses, data lakes, batch/real-time event processing, streaming, data processing (ETL/ELT), data migrations, data visualization and data governance on cloud native architectures.
Experience in conducting technical discovery, business and technical requirements and translating them into efficient and innovative technical architectures that leverage data and AI services.
Preferred qualifications:
Experience implementing MLOps best practices, CI/CD pipelines for ML models and infrastructure as code for deploying data and AI solutions.
Experience in integrating data and AI solutions with existing enterprise systems, on-premises infrastructure and third-party applications.
Experience in optimizing performance, cost-efficiency and scalability of large-scale data processing pipelines and ML inference systems.
Experience with hybrid cloud architectures and multi-cloud strategies, particularly in data integration and migration.
Experience with architectural design and implementation of solutions involving LLMs, Generative AI models or AI agents (e.g., Vertex AI Agent Engine, ADK, LangChain, LlamaIndex).