AI Systems Operations Engineer Enterprise Video Services, Wello, and Brand & Sponsorship Platforms

Lead Software Engineer - Enterprise Video Services, Wello, and Brand & Sponsorship Platforms

As a Lead Software Engineer supporting Enterprise Video Services, Wello, and Brand & Sponsorship platforms, you will drive the design and delivery of scalable, secure solutions that enable enterprise video streaming, on‑demand content, and brand asset management.

You will lead hands-on engineering across platforms supporting webcast streaming, video content management, digital asset governance, and sponsorship activations ensuring high reliability, performance, and alignment with enterprise standards.

Partnering with Marketing, Corporate Communications, Product, Architecture, and Risk & Compliance, you will translate business needs into well‑architected solutions across live/VOD delivery, content lifecycle management, and platforms such as Wello and Brand Central.

This role combines deep technical ownership with engineering excellence to improve platform reliability and enable faster, more effective content creation and distribution at scale.

In this role, you will:

* Lead complex, broad impact initiatives including provision of high-level systems consultation for the technology teams

* Work as key participant in large scale planning of computer systems and network infrastructure for Systems Operations functional area

* Review and analyze complex technical challenges, as well as escalated support issues related to core business solutions that require in depth evaluation of multiple factors, such as alternatives, enhancements, periodic systems reviews, or improvements to existing systems

* Make decisions on technical changes and enhancements

* Consult with engineering team on change design requiring solid understanding of technical process controls or standards that influence and drive new initiatives

* Collaborate and consult with technical peers, colleagues, and mid to more experienced level managers to resolve systems support issues and achieve goals

* Design, build, and deploy AI‑enabled applications using Wells Fargo‑approved enterprise AI platforms and tooling (e.g., Tachyon, Devin, Codequest and Copilot‑based capabilities, and AI development hubs) to solve business and operational problems.

* Develop and integrate Retrieval‑Augmented Generation (RAG), prompt engineering, and model orchestration patterns within approved enterprise environments.

* Collaborate with architecture, data, cybersecurity, and risk partners to ensure AI solutions align with enterprise architecture standards, security controls, and model governance expectations.

* Apply AI responsibly by incorporating model risk management, data privacy, explainability, and ethical AI considerations throughout the solution lifecycle.

* Support experimentation and prototyping in secure, sandboxed environments and transition validated solutions into production‑ready implementations.

* Contribute to reusable AI assets, patterns, and best practices to accelerate enterprise adoption while maintaining consistency and compliance.

* Participate in design reviews, JAD sessions, and architecture or cyber tollgates to articulate AI design decisions and risk mitigations.

Required Qualifications:

* 5+ years of Systems Engineering, Technology Architecture experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

* 2+ years of experience with AI or GenAI solutions within enterprise or regulated environments.

* 2+ years of experience with prompt engineering, LLM application development, or AI orchestration frameworks

Desired

Qualifications:

* Strong software engineering fundamentals (e.g., API development, version control, CI/CD concepts).

* Experience collaborating across product, architecture, cybersecurity, and risk stakeholders.

* Direct experience working with Wells Fargo-approved AI platforms and tools such as Tachyon, Devin, Codequest, CoPilot-based tooling and other internal GenAI development ecosystems or sanctioned AI productivity solutions.

* Experience implementing RAG architectures, secure data access patterns, and model evaluation approaches.

* Familiarity with AI governance, model…