Engineer I,Systems Reliability Engineering
At a glance
Mid-level Site Reliability role at Levi Strauss & Co. Office, Mexico, D.F., Mexico · full-time.
Pay not stated Site Reliability salary
devopsroles summary, based on the employer's posting.
What you'll do
- Keep Product Creation and Master Data platforms running, including FlexPLM, DaVinci, Infor Nexus and MDM services
- Investigate production issues across applications, APIs, queues and file feeds, recording evidence and escalating harder cases
- Watch service health, interface alerts, queue delays and data controls, escalating risks to service or business deadlines
- Maintain incident records, runbooks and dependency maps, then support root-cause reviews and post-incident follow-up
- Use approved Agentic AI tools carefully and help test automation that reduces repetitive operational work
What you bring
- At least three years in production support, application operations, platform engineering or site reliability
- A bachelor's degree in computer science, engineering, information systems or a related field
- Experience supporting critical enterprise applications, PLM, MDM, enterprise data or integration platforms
- Working knowledge of APIs, middleware, queues, batch processing, file feeds, data controls and system dependencies
Who this fits
This role suits an early-career systems reliability engineer who can support business-critical platforms and coordinate with technical and business teams. It is based in Mexico City and reports to the regional Systems Reliability Engineering manager.
From the employer
Calling all originals: At Levi Strauss & Co., you can be yourself — and be part of something bigger. We're a company of people who like to forge our own path and leave the world better than we found it. Who believe that what makes us different makes us stronger. So add your voice. Make an impact. Find your fit — and your future.
Introduction
As an Engineer I, Systems Reliability Engineering, you will provide hands-on production support for Product Creation and Master Data platforms, including FlexPLM, DaVinci, Infor Nexus, MDM services, and related integrations. This ML06 role, based in GCC Mexico, reports directly to the ML08 Manager, Systems Reliability Engineering, LATAM. Working with the regional technical lead, you will protect availability and data integrity through disciplined support, observability, documentation, automation and responsible use of Agentic AI.
About the Job
- Support enterprise applications, APIs, middleware, queues, batch processes and file feeds; investigate incidents, document evidence and escalate complex issues to the ML07 technical lead.
- Monitor application health, queue age, interfaces, alerts and data-control indicators; follow agreed escalation paths when service or business deadlines are at risk.
- Maintain accurate incident records, runbooks, dependency maps and knowledge articles; participate in root cause analysis and blameless post-incident follow-up.
- Follow agreed ways of working for incident, problem and change management, peer-reviewed changes, release validation, recovery procedures and clear cross-region handovers.
- Use approved Agentic AI tools for investigation, knowledge retrieval and runbook assistance; validate outputs, protect sensitive data and obtain required approval before operational actions.
- Collaborate with product, engineering, infrastructure, enterprise data, ERP and business teams; identify repetitive work and contribute tested automation improvements.
About You
- 3+ years of experience in IT production support, application operations, platform engineering or Site Reliability Engineering.
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field.
- Experience supporting business-critical enterprise applications, PLM, MDM, enterprise data or integration platforms, with the ability to learn application-specific processes.
- Understanding of APIs, middleware, queues, batch processing, file feeds, data controls and upstream and downstream dependencies.
- Knowledge of ITSM, incident and change practices; experience using AI copilots or automation responsibly, verifying results and escalating uncertainty.