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.

Key Responsibilities:
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 comply with enterprise model risk, data governance, technology risk, security, and operational requirements.

Agentic AI Innovation

• Drive the integration of Generative AI, RAG pipelines, and agentic AI capabilities into enterprise workflows and platform services.
• Architect full-stack agentic AI solutions, ranging from conversational experiences to complex multi-agent systems.
• Evaluate and apply technologies such as Large Language Models, vector databases, prompt engineering, orchestration frameworks, Model Context Protocol (MCP), and agent-to-agent patterns.
• Promote modern development approaches, rapid prototyping, and AI-assisted engineering practices to accelerate responsible innovation.
• Convert successful prototypes into secure, scalable, governed, and production-ready enterprise solutions.
• Identify opportunities to improve operational service levels, engineering productivity, and business outcomes through automation.

Cross-Functional Collaboration

• Partner with data scientists, AI engineers, MLOps engineers, data engineers, architects, cybersecurity teams, model governance teams, and application development teams.
• Serve as a trusted advisor to senior leadership on AI strategy, platform architecture, modernization priorities, and technical investment decisions.
• Facilitate architecture discussions and build alignment across business, engineering, governance, infrastructure, and operations teams.
• Clearly communicate complex technical concepts, architecture decisions, risks, trade-offs, and recommendations to both technical and executive audiences.
• Ensure platform capabilities are aligned with enterprise priorities, customer needs, and measurable business outcomes.

Required Qualifications:

• 7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• 5+ years of hands-on programming or scripting experience using Python, Java, Shell scripting, or similar technologies.
• 5+ years of experience implementing Infrastructure as Code using Terraform, Crossplane, or equivalent solutions.
• 5+ years of hands-on experience with OpenShift Container Platform, Google Cloud Platform, Microsoft Azure, or comparable enterprise cloud platforms.
• 5+ years of experience designing and implementing enterprise-grade automation solutions using technologies such as Ansible, Harness CD, GitHub Actions, Playwright, or equivalent tools.
• 2+ years of experience designing and developing AI, Generative AI, or agentic automation solutions.

Desired Qualifications:

• Proven experience architecting, building, and operating enterprise-scale AI/ML platforms across hybrid and multi-cloud environments.
• Deep understanding of enterprise AI/ML architecture, including data and compute separation, platform interoperability, and integration patterns.
• Strong expertise in the end-to-end Model Development Lifecycle (MDLC), MLOps, model governance, monitoring, and operationalization.
• Experience with cloud AI platforms such as GCP Vertex AI, Azure ML, and enterprise on-premises AI/ML environments.
• Strong hands-on expertise in Kubernetes platforms, including OpenShift (OCP) and Google Kubernetes Engine (GKE).
• Experience designing and implementing Generative AI, RAG, Agentic AI, and multi-agent solutions.
• Knowledge of LLMs, prompt engineering, vector databases, embedding models, orchestration frameworks, MCP, and agent-to-agent architectures.
• Experience building AI solutions using frameworks such as LangGraph, CrewAI, Microsoft AutoGen, LangChain, and Chainlit.
• Strong understanding of security architecture, data protection, technology risk, regulatory compliance, and data governance controls.
• Experience delivering enterprise AI modernization and model migration initiatives from on-premises to cloud-native platforms.
• Strong programming skills in Python and SQL, with experience using machine learning frameworks such as TensorFlow or PyTorch.
• Ability to influence technical strategy and architecture decisions across multiple organizations and senior leadership teams.
• Excellent communication, stakeholder management, and executive presentation skills.
• Cloud, AI, or Kubernetes-related certifications (AWS, GCP, Azure, CKA, or equivalent) preferred.

Job Expectations:

• This position is not eligible for visa sponsorship.
• Ability to work on-site at an approved location.
• Relocation assistance is not available for this position.

Locations:

• 1755 Grant St., Concord, California
• 300 S. Brevard St., Charlotte, North Carolina
• 333 Market St., San Francisco, California

Pay Range
Reflected is the base pay range offered for this position. Pay may vary depending on factors including but not limited to demonstrated examples of prior performance, skills, experience, or work location. Employees may also be eligible for incentive opportunities.
$159,000.00 - $305,000.00
Benefits
Wells Fargo provides eligible employees with a comprehensive set of benefits, many of which are listed below. Visit Benefits - Wells Fargo Jobs for an overview of the following benefit plans and programs offered to employees.

• Health benefits
• 401(k) Plan
• Paid time off
• Disability benefits
• Life insurance, critical illness insurance, and accident insurance
• Parental leave
• Critical caregiving leave
• Discounts and savings
• Commuter benefits
• Tuition reimbursement
• Scholarships for dependent children
• Adoption reimbursement

Posting End Date:
1 Sep 2026
* Job posting may come down early due to volume of applicants.
We Value Equal Opportunity
Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.
Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit's risk appetite and all risk and compliance program requirements.
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