Workforce Compute Ops SRE Lead Infrastructure Engineer

Wells Fargo•Irving, TX

4d•Onsite

About The Position

Wells Fargo is seeking a Lead Site Reliability Engineer to provide technical leadership, mentorship, and guidance to a team responsible for the stability, reliability, availability, performance, and continuous improvement of enterprise workplace technology platforms. The role establishes and promotes Site Reliability Engineering (SRE) standards and best practices across observability, service level indicators (SLIs), service level objectives (SLOs), error budgets, incident and problem management, automation, and operational resilience. It uses telemetry and data-driven insights to identify risks, reduce incident frequency and recurrence, and drive reliability improvements. The engineer will help improve workplace technology platform stability using intelligent automation, Agentic AI capabilities, and low-code/no-code solutions. This includes technical leadership for AI-assisted incident triage, root cause analysis, workflow automation, proactive remediation, and self-healing capabilities to improve operational efficiency and the end-user experience. The position is onsite at one of the posted locations. Sponsorship is not available. The posting end date is October 21, 2026, though the job posting may come down earlier due to applicant volume.

Requirements

5+ years of Technology Infrastructure Engineering and Solutions experience, or equivalent demonstrated through work experience, training, military experience, education, or a combination of these.
5+ years of experience developing software, automation, or data processing solutions using Python.
5+ years of experience with observability and monitoring technologies such as Splunk, Grafana, Prometheus, Elastic, or similar tools.
Must be able to work onsite at one of the posted locations.

Nice To Haves

Experience providing technical leadership and mentoring engineers.
Strong knowledge of SRE principles and practices, including observability, SLIs, SLOs, error budgets, incident management, problem management, and operational resilience.
Experience developing Generative AI or Agentic AI solutions.
Knowledge of large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), AI-assisted workflows, and API-based AI integrations.
Experience with workflow automation and low-code/no-code platforms.
Experience using operational telemetry and data analytics to identify trends, anomalies, and opportunities for proactive remediation.
Experience with observability and monitoring platforms such as Splunk, Grafana, Prometheus, Elastic, or similar technologies.
Experience with containers, Kubernetes, and Infrastructure as Code practices.
Strong problem-solving, communication, and collaboration skills.
Ability to provide technical direction and influence engineering practices across teams.

Responsibilities

Lead complex initiatives to develop infrastructure solutions that support business applications.
Participate in projects to improve, modernize, and enhance technology infrastructure.
Evaluate internal and external software solutions to support target-state architecture objectives.
Review and analyze high-impact outages and implement processes to reduce future operational risk.
Design, build, deploy, and maintain infrastructure solutions in collaboration with technology teams and third-party vendors.
Design, code, test, debug, and document solutions using Agile development practices.
Influence technical designs and implementation plans while identifying project risks and resource requirements.
Provide technical leadership and guidance to engineers and partners across the organization.
Direct risk and control activities by ensuring adherence to policies, procedures, and operational standards.
Recommend solutions that improve efficiency, manage costs, and achieve business objectives.
Collaborate with peers, leaders, customers, and vendors to resolve issues and deliver technology solutions.
Establish and promote SRE practices across observability, SLIs, SLOs, error budgets, incident and problem management, automation, and operational resilience.
Use telemetry and data-driven insights to identify risks, reduce incident frequency and recurrence, and drive reliability improvements.
Provide technical leadership for AI-assisted incident triage, root cause analysis, workflow automation, proactive remediation, and self-healing capabilities.