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About the Role

Wells Fargo is seeking a Principal Engineer for Tachyon Cortex AI Engineering to 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.

Responsibilities

Act as a trusted technical advisor to senior leadership on highly complex AI/ML platform, application, infrastructure, 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.
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).
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.
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.
Partner with data scientists, AI engineers, MLOps engineers, data engineers, architects, cybersecurity teams, model governance teams, and application development teams.
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.

Requirements

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.

Skills

AI/ML platform architecture
AI compute environments
Data engineering
Cloud-native architecture
Model Development Lifecycle (MDLC)
MLOps
Generative AI
Retrieval-Augmented Generation (RAG)
Agentic AI systems
GCP Vertex AI
Azure Machine Learning
Kubernetes
OpenShift Container Platform (OCP)
Google Kubernetes Engine (GKE)
Ansible
Harness CD
GitHub Actions
Playwright
Python
SQL
TensorFlow
PyTorch
Terraform
Crossplane
Large Language Models (LLMs)
Vector databases
Prompt engineering
Orchestration frameworks
Model Context Protocol (MCP)
Agent-to-agent patterns
LangGraph
CrewAI
Microsoft AutoGen
LangChain
Chainlit

Location

Concord, California
Charlotte, North Carolina
San Francisco, California

Work Type

On-site

Experience Level

Principal Engineer

Salary/Compensations

$159,000.00 - $305,000.00

Benefits

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

About the Company

Wells Fargo is an equal opportunity employer.
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.

Equal Opportunity

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.
To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo.
Wells Fargo maintains a drug free workplace.
Third-Party recordings are prohibited unless authorized by Wells Fargo.
Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.