Cybersecurity AI Principal Engineer

Wells Fargo & Company•Charlotte, NC

1d•$159,000 - $305,000•Hybrid

About The Position

Wells Fargo is seeking a Principal Engineer for its Cybersecurity AI team to help design and evolve VoltAIre, a Cyber AI platform that provides secure, highly available, scalable, isolated, and resilient execution environments for a broad range of Cyber AI workloads. VoltAIre spans Google Cloud and on-premises cloud (OCP) and supports workloads such as SAST and DAST vulnerability scanners, knowledge assistants, AI-enabled applications, and emerging Cyber AI capabilities across experimentation, on-demand execution, and production environments. This role combines platform architecture, distributed systems, application architecture, cloud infrastructure, and hands-on software engineering. The Principal Engineer determines how applications should be designed, where workloads should run, and which infrastructure, isolation, resiliency, scalability, and technology patterns fit each workload. The role provides technical leadership for the platform, drives solutions from architecture and prototyping through implementation and production operations, and advises enterprise stakeholders on complex technical decisions. It also mentors senior and staff engineers and helps establish reusable engineering patterns. The position has a hybrid work schedule. It is not eligible for visa sponsorship. The listed base salary ranges are $159,000–$254,000 in Charlotte, NC, and $191,000–$305,000 in Menlo Park, CA; the overall stated base pay range is $159,000–$305,000. Employees may also be eligible for incentive opportunities.

Requirements

At least 7 years of engineering experience, or equivalent demonstrated through work experience, training, military experience, education, or a combination.
At least 7 years of senior engineering experience, including architecture and hands-on delivery of large-scale platforms, distributed applications, and production systems.
At least 3 years of hands-on experience with cloud architectures, including Google Cloud Platform, Microsoft Azure, AWS, or comparable enterprise cloud technologies.
Demonstrated experience designing, building, and operating applications and services on Kubernetes or comparable container orchestration platforms.
Experience with distributed systems and application architecture, including networking, load balancing, APIs, data flows, concurrency, persistence, scalability, and failure handling.
Experience designing solutions with SQL and/or NoSQL data technologies and evaluating choices against workload and production requirements.
Experience designing highly available and resilient production systems, including scaling, redundancy, failure recovery, and disaster recovery considerations.
Ability to work at both hands-on technical depth and strategic architecture level, translating complex technical decisions and trade-offs for engineering teams and leadership.
Proficiency in AI-assisted development and analysis tools, such as GitHub Copilot and approved code-centric agents.
Ability to validate and integrate AI-assisted outputs using sound technical judgment and account for model limitations, security risks, and operational considerations.
Ability to apply AI responsibly in development and production environments in alignment with security, compliance, privacy, and ethical standards.

Nice To Haves

Experience designing platforms that support multiple workloads or tenants with differing security, isolation, availability, scalability, performance, and resiliency requirements.
Experience with hybrid or multi-cloud architectures spanning public cloud and on-premises infrastructure.
Strong software engineering background in one or more languages such as Rust, Go, Python, or Java, or comparable languages; systems programming experience is particularly valuable.
Experience designing and operating large-scale Kubernetes platforms, distributed workloads, and cloud-native applications in production.
Experience with observability platforms and practices, including logging, metrics, monitoring, telemetry, tracing, and production troubleshooting.
Experience designing active/active, active/passive, regional redundancy, disaster recovery, and other high-availability patterns.
Experience with application and infrastructure performance engineering, capacity management, elastic scaling, and runtime optimization.
Experience working within agile product operating models in large enterprise environments, including agile software delivery and cloud infrastructure.
Demonstrated ability to mentor senior and staff engineers and raise the technical engineering bar across an organization.
Strategic thinking, analytical problem-solving, and sound technical judgment, including evaluating alternatives and making pragmatic architectural trade-offs.
Curiosity and an innovation mindset for advancing Generative AI, Agentic AI, and Cyber AI capabilities in regulated enterprise environments.
Experience with cybersecurity platforms, vulnerability management, SAST, DAST, security scanners, AI/ML platforms, or compute-intensive distributed workloads.
Organizational savvy and customer centricity, with the ability to navigate complex enterprise environments and align technology decisions to business and cybersecurity outcomes.
Financial services or other highly regulated industry experience.

Responsibilities

Advise leadership and influence architecture across applications, cloud infrastructure, networking, information security, data platforms, Kubernetes, and distributed systems for complex Cyber AI workloads.
Provide technical leadership for VoltAIre and establish reusable platform capabilities and architectural patterns across cloud and on-premises environments.
Design and influence Cyber AI applications, including vulnerability scanners, knowledge assistants, security services, and AI-enabled applications, from architecture through production deployment and operation.
Assess workload characteristics and make fit-for-purpose decisions concerning security, isolation, availability, scalability, resiliency, performance, operational complexity, and cost.
Define workload deployment and isolation patterns, including shared Kubernetes clusters with namespace-based isolation, dedicated clusters, separate regions, and separate infrastructure or cloud providers.
Design highly available and resilient architectures, including elastic scaling, active/active, active/passive, and disaster recovery patterns appropriate to workload needs.
Provide architectural direction across Kubernetes, load balancing, application networking, compute, storage, SQL and NoSQL technologies, observability, and distributed application design.
Design or re-engineer critical software components when performance, security, concurrency, reliability, or resource efficiency require deeper systems-level engineering.
Establish and evolve platform capabilities for logging, monitoring, telemetry, observability, security, adaptive runtime optimization, and intelligent workload routing.
Lead the strategy and resolution of complex technical challenges spanning applications, infrastructure, cloud, cybersecurity, and enterprise technology environments.
Translate technology experience and business requirements into scalable engineering solutions and clearly explain architectural alternatives and trade-offs.
Drive solutions from architecture and prototyping through implementation, production deployment, troubleshooting, and continuous optimization.
Maintain knowledge of emerging technologies and engineering practices across cloud-native platforms, distributed systems, cybersecurity, Generative AI, Agentic AI, and systems engineering, and recommend innovations.
Engage with engineers, architects, product teams, cybersecurity partners, and leadership across the enterprise as an expert technical advisor.
Mentor senior and staff engineers and establish reusable engineering patterns and practices across platform and application engineering.
Use AI-assisted development and analysis tools to accelerate system design, coding, testing, analysis, and troubleshooting; validate outputs and apply AI responsibly in accordance with security, compliance, privacy, and ethical standards.