AIOps Engineer

Job Description :

Key Responsibilities :

- Build, integrate, and optimize AIOps platforms to improve incident detection, root cause analysis, and automated remediation.

- Develop and maintain automation scripts for repetitive operational tasks using Python, Shell, or PowerShell.

- Implement intelligent anomaly detection and predictive analytics using ML/AI models.

- Configure, manage, and enhance monitoring tools such as Datadog, New Relic, Dynatrace, Prometheus, Grafana, Splunk, ELK, or similar.

- Develop advanced dashboards and observability pipelines for logs, metrics, traces, and events.

- Ensure high availability and performance of monitoring and alerting systems.

- Lead triage during critical incidents and work with engineering teams to reduce MTTR.

- Perform root cause analysis using AI-driven insights and create preventive action plans.

- Build self-healing workflows and automated incident response systems.

- Work closely with DevOps teams to integrate AIOps in CI/CD pipelines.

- Manage and support cloud infrastructure on AWS / Azure / GCP.

- Implement and optimize cloud-native services for logging, monitoring, and autoscaling.

- Build and manage data pipelines for operational data across logs, metrics, traces, events, and alerts.

- Work with ML engineers to integrate models into operational workflows.

- Ensure data quality, integrity, and real-time processing for AIOps systems.

- Collaborate with SRE, DevOps, Infrastructure, and Security teams to enhance operational intelligence.

- Create clear documentation, runbooks, workflows, and automation playbooks.

- Train internal teams on AIOps processes and automation tools.

Required Skills & Experience :

Technical Expertise :

- 4+ years of experience in AIOps, DevOps, SRE, or Cloud Operations roles.

- Hands-on experience with at least one AIOps or observability platform :

1. Dynatrace, Moogsoft, Datadog, BigPanda, Splunk ITSI, New Relic, Elastic APM, etc.

2. Strong scripting/programming skills in Python, Shell, Go, or PowerShell.

- Experience with ML/AI models for anomaly detection, forecasting, or event correlation (nice-to-have but preferred).

- Strong knowledge of cloud platforms (AWS, Azure, GCP) and cloud-native monitoring services.

- Strong understanding of Kubernetes, Docker, and microservices monitoring.

- Experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, Azure DevOps, etc.

- Strong understanding of Infrastructure as Code tools : Terraform, CloudFormation, Ansible.

- Solid knowledge of logs, metrics, traces, APM tools, synthetic monitoring, and alerting frameworks.

- Experience with log aggregation tools (Splunk, ELK, Loki, etc.).

Soft Skills :

- Strong analytical thinking and problem-solving abilities.

- Excellent communication skills to collaborate with cross-functional teams.

- Ability to work in a fast-paced environment and handle critical incidents calmly.