We are looking for an experienced AIOps Engineer to join our team and leverage Artificial Intelligence, Machine Learning, and automation to improve IT operations, enhance system reliability, and reduce operational complexity.
The ideal candidate will have strong experience in applying AI/ML techniques to infrastructure monitoring, anomaly detection, predictive analytics, root cause analysis, and automated incident response.
Key Responsibilities
· Design and implement AI-driven monitoring and observability solutions for IT infrastructure and applications.
· Develop machine learning models and algorithms for anomaly detection and predictive failure analysis.
· Implement intelligent event correlation and alert noise reduction mechanisms.
· Build automated incident detection, classification, prioritization, and response workflows.
· Apply AI techniques to identify root causes of system failures and performance degradation.
· Integrate AIOps solutions with existing monitoring, logging, and IT Service Management (ITSM) platforms.
· Develop predictive analytics solutions to identify potential system outages and infrastructure bottlenecks.
· Automate operational workflows and incident remediation processes.
· Monitor system health, application performance, infrastructure capacity, and service availability.
· Collaborate with DevOps, SRE, Infrastructure, Security, and Software Engineering teams.
· Continuously optimize operational efficiency, system reliability, and Mean Time to Resolution (MTTR).
Requirements & Qualifications
· 5+ years of experience in AIOps, DevOps, SRE, IT Operations, or a related field.
· Strong programming and scripting skills in Python.
· Solid understanding of Machine Learning, statistical analysis, and anomaly detection techniques.
· Hands-on experience with monitoring and observability platforms such as Prometheus, Grafana, Datadog, Dynatrace, or New Relic.
· Experience with centralized logging and log analytics tools such as ELK Stack, OpenSearch, or Splunk.
· Knowledge of event correlation, alert management, and root cause analysis.
· Experience with incident management and ITSM platforms such as ServiceNow or PagerDuty.
· Strong understanding of cloud infrastructure (AWS, Azure, or GCP).
· Experience with Docker, Kubernetes, and distributed systems.
· Familiarity with CI/CD pipelines and Infrastructure as Code.
· Understanding of ITIL principles, incident management, and service reliability practices.
· Strong analytical, troubleshooting, and problem-solving skills.
Good to Have
· Experience with AIOps platforms and AI-driven IT operations solutions.
· Knowledge of time-series forecasting and predictive maintenance.
· Experience implementing automated remediation and self-healing infrastructure.
· Familiarity with Large Language Models (LLMs) for intelligent incident analysis and operational assistants.
· Experience with OpenTelemetry and distributed tracing.
· Knowledge of Site Reliability Engineering (SRE) practices and SLO/SLI management.
