Job description

We are Lenovo. We do what we say. We own what we do. We WOW our customers. Lenovo is a US$83 billion revenue global technology powerhouse, ranked #153 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world’s largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. Lenovo’s continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY). To find out more visit www.lenovo.com and read about the latest news via our StoryHub . About Our Team We are looking for an AI Reliability Operations Engineer to support the operational health of Qira's production and non-production systems. Qira is Lenovo’s cross-device Personal AI that works across phones, PCs, and other Lenovo and Motorola products. This role spans system monitoring, alert response, incident response, and observability across the full AI stack, including model performance, inference pipelines, and cloud services. You will also have visibility into SDLC operations across staging and pre-production environments, helping ensure that releases and configuration changes land cleanly. This is a foundational role in keeping Qira stable and available for users around the world. Location: Onsite in Chicago, IL (Hybrid, 3 days onsite, 2 days remote) What You'll Do Operations Domain • Perform incident response: contain issues and work cross-functionally with dev teams to fully resolve them. • Monitor system health via Grafana dashboards, catching issues early and verifying resolution. • Serve as point of contact for change requests (CRs), triaging bug tickets from internal testers to the correct dev group. • Keep incident and CR records clear and accurate in ticketing systems. Monitoring and Observability • Monitor production and non-production systems using observability dashboards, alerting tools, and AI-specific signals, including model performance, inference latency, and data pipeline health. • Watch proactively for early warning signals across Qira's cloud services, device integrations, and AI components, not just respond to alerts after they fire. • Review alert thresholds, update runbooks, and flag procedural gaps to the shift lead or SRE. This role is expected to improve the process, not just execute it. Build Tooling and Documentation • Build and improve tooling and scripts to reduce manual, repetitive work across incident and CR handling. • Maintain documentation standards: keep runbooks, CR records, and process docs accurate and usable by anyone on the team. Release Support • Observe and report on SDLC operations across staging and pre-production environments, flagging anomalies and supporting engineering teams during releases and configuration changes. • Verify system health before and after deployments and configuration changes, and assist engineering with deployment checks. Basic Qualifications • Direct experience in incident response or an SRE-adjacent role, not just monitoring or support. • Experience with observability tools such as Grafana, Datadog, or cloud-native dashboards. • Experience with alerting tools such as PagerDuty or OpsGenie, and ticketing systems such as Jira or ServiceNow. • Experience Troubleshooting: can isolate where a problem actually lives (which service, which layer) without being handed the answer. • Experience in Azure, including core cloud concepts and how services in an Azure environment are monitored. Preferred Qualifications • Experience in a technical operations, SRE, or production support environment. • Exposure to AI or ML systems, including awareness of how model quality and data pipelines are monitored. • Basic scripting ability or comfort reading and adapting existing scripts and runbook commands. • Experience working across time zones in a globally distributed team. • Working knowledge of SRE concepts: P50/P95/P99 latency, MTTA/MTTM/MTTR (MTTx), the four golden signals (latency, traffic, errors, saturation), and how to apply them to triage. • Clear, precise written communication in English, including accurate incident updates under pressure. • Ability to work assigned on-call coverage What Success Looks Like A successful AI Reliability Operations Engineer detects issues early, responds to alerts quickly, performs accurate initial triage, and keeps clear and complete records across production and non-production environments. Their work directly supports system uptime and ensures that Qira delivers a reliable, consistent experience for users at all times The base salary budgeted range for this position is $80K - $90K. Individuals may also be considered for bonus and/or commission. Lenovo’s various benefits can be found on www.lenovobenefits.com. We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, religion, sexual orientation, gender identity, national origin, status as a veteran, and basis of disability or any federal, state, or local protected class. PAY TRANSPARENCY The anticipated annual compensation range for this position is 80,000–90,000 USD . Final compensation will be based on relevant experience, skills, and business considerations. Individuals may also be considered for bonuses and/or commissions. Lenovo’s various benefits can be found at www.lenovobenefits.com

Requirements

Must-haves

We are Lenovo. We do what we say. We own what we do. We WOW our customers.
To find out more visit www.lenovo.com and read about the latest news via our StoryHub .
Operations Domain
Perform incident response: contain issues and work cross-functionally with dev teams to
fully resolve them.
Monitor system health via Grafana dashboards, catching issues early and verifying resolution.
Serve as point of contact for change requests (CRs), triaging bug tickets from internal testers
to the correct dev group.
Keep incident and CR records clear and accurate in ticketing systems.
Monitoring and Observability
Monitor production and non-production systems using observability dashboards,
alerting tools, and AI-specific signals, including model performance, inference latency,

Nice-to-haves

Experience in a technical operations, SRE, or production support environment.
Exposure to AI or ML systems, including awareness of how model quality and data pipelines
are monitored.
Basic scripting ability or comfort reading and adapting existing scripts and runbook
Experience working across time zones in a globally distributed team.
Working knowledge of SRE concepts: P50/P95/P99 latency, MTTA/MTTM/MTTR (MTTx),
the four golden signals (latency, traffic, errors, saturation), and how to apply them to
Clear, precise written communication in English, including accurate incident updates
under pressure.
Ability to work assigned on-call coverage
What Success Looks Like
A successful AI Reliability Operations Engineer detects issues early, responds to alerts quickly,

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aijiraazureml

Platform: avature
Effort: medium

Benefits

Benefits detected in the listing.

Vision