Job Summary

Location: Chennai

Were looking for someone who genuinely wants to understand why systems fail, not just respond to alerts. This role sits at the crossroads of cloud infrastructure and production reliability. You own monitoring, handle on-call, and be the person who digs in when things go wrong. At the same time, were building an AI-powered pipeline monitoring tool and need someone curious enough to contribute to shaping it, not just watching over it.

Responsibilities

- Handle the on-call rotation and own incidents end-to-end triage, mitigation, escalation where needed, and clean resolution. You dont pass the baton and disappear.
- Write clear, structured RCAs after every significant incident what happened, when, why, and what changes going forward. These go to clients, so they need to work for both an engineer and a non-technical reader.
- Maintain and improve the monitoring stack across environments dashboards, alerting rules, log pipelines, and distributed traces. Treat noisy alerts as a problem to fix, not something to mute.
- Provision and manage cloud infrastructure on AWS using Terraform. This is a hands-on role not just reviewing what others set up.
- Work with Kubernetes across multiple environments, debugging pod and node issues.
- Monitor CI/CD pipeline health via Jenkins and support teams using Rancher for workload and cluster management.
- Track application performance using APM tooling and JVM metrics: spot anomalies, investigate degradation, and flag systemic issues before they become incidents.
- Contribute to the AI monitoring tool initiative: prototype, test, iterate. This is early-stage work and needs someone willing to figure things out, not just execute a finished design.

Tech Stack

- Cloud & Infrastructure: AWS Kubernetes Terraform Linux
- Observability & Metrics: Prometheus Grafana APM (Datadog / New Relic / Kfuse) JVM Metrics & GC Analysis ELK / EFK Stack Distributed Tracing
- CI/CD & Platform: Jenkins ArgoCD Rancher Git Docker




- Positive to Have(Not Mandatory): Python / Bash scripting OpenTelemetry Zenduty / OpsGenie ML / AI basics

Expectations

- On-call here is real. Incidents happen outside business hours and when they do, it your responsibility to pick them up and drive them forward. That not unusual for this type of role but we want to be direct about it upfront.
- Client expectations are high. You produce RCAs, incident timelines, and status communications that clients read closely. Your writing needs to be clear, structured, and free of vagueness. We investigated and fixed the issue isnt good enough. What was the issue, why did it happen, what was the business impact, and what prevents recurrence.
- We expect precision regarding your own work. After a change, an incident, or a deployment, you should be able to clearly explain what you did and why without being prompted. Ownership doesnt end when the alert clears.

Who we're looking for

- The ideal candidate should have 2.5 years to 5 years of work experience.
- Strong fundamentals. You understand how distributed systems actually behave under load, not just that a dashboard went red. You can read logs, metrics, and traces together.
- Ownership without prompting. If you find a gap in monitoring coverage, you close it. If an RCA feels incomplete, you go back and make it precise. You dont wait to be asked.
- Writes clearly under pressure. During an incident, your updates should help not add noise. After one, your documentation should be good enough that anyone picking it up six months later understands what happened.
- Curious about what comes next. The AI tooling initiative needs someone interested in figuring it out, not just waiting for a ticket. Some comfort with experimentation and ambiguity goes a long way here.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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