Production Support Lead

Professional Services -Service Practice

Full-time · Remote (India / US / EU)
Experience : 5-8 years

About Lyzr

Lyzr.ai's agentic AI platform powers intelligent, autonomous workflows for enterprise clients. Production Support Engineers are the front line that keeps those workflows healthy — triaging incidents, resolving tickets, digging into logs, and escalating the right issues to the right teams before clients feel the pain.

This role suits someone who thrives in a fast-paced technical environment, takes ownership seriously, and genuinely enjoys the detective work of diagnosing why something broke in production. You will work within a global follow-the-sun support model, reporting to the Production Support Lead.

What you’ll do

Incident command & escalation

• Own the full incident lifecycle — detection, triage, war-room coordination, resolution, and post-mortem — for P1/P2 issues across all production tenants.

• Act as the primary escalation point for Production Support Engineers; make the call on severity reclassification and client communication timing.

• Drive RCA completion within SLA windows and ensure corrective actions are tracked to closure in Jira/Confluence.

• Maintain and continuously improve the P1 runbook library, escalation trees, and on-call rotation schedules.

Team leadership & operations

• Manage and mentor a team of 3–6 Production Support Engineers; run weekly 1:1s, set KPIs, and own the performance review cycle.

• Build and optimise the shift rota for 24x7x365 follow-the-sun coverage across India, EU, and US time zones.

• Define and track operational metrics: MTTR, SLA attainment by priority tier, re-open rate, and backlog aging.

• Partner with Engineering and Platform teams to advocate for supportability improvements, observability tooling, and bug-fix prioritisation.

Client & commercial accountability

• Serve as the named support contact for strategic accounts during critical incidents; provide executive-level written updates under pressure.

• Review monthly SLA performance reports with client stakeholders; identify systemic patterns and propose proactive remediation.

• Contribute to SLA definition in new SOWs, ensuring commitments are operationally deliverable.

• Support the renewal and expansion process by demonstrating support maturity and service quality data.

Process & tooling

• Own the support toolchain: ticketing (Jira Service Management or equivalent), monitoring dashboards, alerting rules, and on-call tooling (PagerDuty / OpsGenie).

• Establish knowledge management practices — internal runbooks, known-error database, and a tiered FAQ — to reduce repeat escalations to Engineering.

• Define and enforce severity classification criteria and ticket hygiene standards across the team.

What you bring

Additionally, you will have:

• Hands-on familiarity with cloud infrastructure (AWS / GCP / Azure) and container environments (Kubernetes, Docker).

• Ability to read logs, traces, and basic Python/SQL to independently diagnose issues before engaging Engineering.

• Bonus: experience supporting multi-tenant SaaS at scale, or prior work with AI/ML pipelines in production.

• Bonus: familiarity with enterprise client SLA frameworks — P1/P2/P3 tiering, OLA/UC structures.