Principal Engineer - AI Engineering

About this role: Wells Fargo is seeking a Principal Engineer for Tachyon Cortex AI Engineering within the Digital Technology and Innovation organization. This organization supports the evolution of digital platforms and accelerates the integration of innovation into enterprise and customer-facing capabilities. The Principal Engineer will provide strategic and hands-on technical leadership for the architecture, development, modernization, and operationalization of enterprise-scale predictive and generative AI solutions across hybrid and multi-cloud environments . This role requires deep engineering expertise, architectural vision, and the ability to influence complex technology decisions across multiple organizations. The ideal candidate will be a seasoned AI/ML platform architect with proven hands-on experience designing, building, and operating enterprise AI platforms. The candidate should have expertise in AI compute environments, data engineering, cloud-native architecture, Model Development Lifecycle (MDLC), MLOps, Generative AI, Retrieval-Augmented Generation (RAG), and agentic AI systems. The role will drive the continued evolution of the Tachyon Cortex enterprise AI/ML platform , including the modernization and migration of models from legacy and on-premises environments to secure, scalable, and compliant cloud-native platforms. The Principal Engineer will also guide enterprise architecture patterns for data and compute separation, platform integration, data protection, security, technology risk, and data governance. In this role, you will: Act as a trusted technical advisor to senior leadership on highly complex AI/ML platform, application, infrastructure, data, security, and cloud engineering decisions. Lead the strategy and resolution of unique enterprise challenges requiring in-depth evaluation across multiple technology areas and organizations. Translate business objectives, enterprise technology strategy, regulatory requirements, and emerging AI capabilities into scalable engineering solutions. Provide vision, direction, and technical expertise for innovative, long-term, and enterprise-scale AI solutions. Maintain knowledge of industry practices and emerging technologies, recommending innovations that improve operational effectiveness or provide a competitive advantage. Strategically engage with professionals and leaders across the enterprise and influence technical decisions, architecture standards, and modernization roadmaps.

Platform Architecture and Engineering Leadership Lead the architecture and continued evolution of the Tachyon Cortex enterprise AI/ML platform across GCP Vertex AI, Azure Machine Learning, Kubernetes-based environments, and on-premises AI/ML platforms. Design scalable, resilient, secure, and compliant AI/ML platform capabilities across hybrid and multi-cloud environments. Define enterprise architecture patterns for data and compute separation, distributed processing, platform interoperability, and data integration. Provide hands-on technical leadership for the design, implementation, and operationalization of enterprise AI/ML platforms. Establish reusable architecture patterns, engineering standards, reference implementations, and platform guardrails. Apply deep expertise in Kubernetes and container orchestration platforms, including OpenShift Container Platform (OCP) and Google Kubernetes Engine (GKE). Model Lifecycle Management Oversee the end-to-end Model Development Lifecycle, including data preparation, feature engineering, model development, validation, deployment, monitoring, governance, and retirement. Establish scalable MLOps capabilities and automated engineering workflows that improve model delivery, reliability, and operational support. Implement proactive, event-driven model monitoring, performance management, observability, and drift detection. Define platform capabilities that support both predictive AI and Generative AI workloads. Ensure AI solutions