Production Support Engineer
Confirmed on the employer's own hiring board on Sep 26, 2026. First seen by Alion on Apr 24, 2026.
Professional Services - Support Practice
Full-time · Remote (India / US / EU)
Experience : 2-5 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 response & triage
Monitor production dashboards and alerts; acknowledge, classify (P1-P3), and triage incoming incidents within SLA response windows.
Perform first-level diagnosis using logs, traces, and monitoring tools (Datadog / Grafana / CloudWatch) to isolate root cause or rule out environmental issues.
Execute approved runbook steps to resolve known issues independently; escalate novel or high-severity issues to the Lead with a clear diagnostic summary.
Maintain accurate, time-stamped ticket updates throughout the incident lifecycle so clients and internal stakeholders always have visibility.
Service request fulfilment
Handle client service requests: configuration changes, access provisioning, agent re-deployments, and data queries within approved change management guardrails.
Validate and document completed requests, ensuring audit trails are maintained in the ticketing system.
Identify recurring requests that could be automated or self-served, and flag them to the Lead for process improvement.
Monitoring & proactive health checks
Run scheduled health checks on production agent pipelines, API integrations, and data connectors; raise pre-emptive alerts for degradation trends.
Maintain and update monitoring dashboards; propose new alert thresholds based on observed patterns.
Participate in post-mortems and contribute findings to the known-error database and runbooks.
Knowledge & collaboration
Document solutions to new issues in the internal knowledge base; keep existing runbooks accurate and up to date.
Collaborate with Engineering, Platform, and Customer Success teams during handoffs, providing clear reproduction steps and log artefacts.
Participate in the on-call rotation (shift-based); expected availability for P1 escalations during assigned windows.
What you bring
Experience: 2-5 years in application / production support or a NOC environment
Domain: SaaS or cloud-hosted platform support; AI/ML familiarity a strong plus
Technical: Log analysis, API debugging, SQL queries, basic Python / shell scripting
Monitoring: Datadog, Grafana, CloudWatch, or equivalent observability tools
Ticketing: Jira Service Management, ServiceNow, or Zendesk
Cloud basics: AWS / GCP / Azure fundamentals; Docker / Kubernetes awareness
Additionally, you will have:
A methodical, structured approach to troubleshooting - you document what you tried, not just what worked.
Clear written communication: ticket updates, client-facing messages, and handover notes that leave no ambiguity.
Comfort working across time zones and collaborating asynchronously with distributed teams.
Bonus: exposure to LLM-based or agentic AI systems, prompt engineering, or RAG pipelines in production.
Bonus: ITIL Foundation certification or equivalent incident management training.