AI Operations (AIOps) Engineer

Job Summary

We are seeking a highly skilled AI Operations (AIOps) Engineer to join our Technology Operations team. The ideal candidate will leverage Artificial Intelligence (AI), Machine Learning (ML), and automation technologies to enhance IT operations, improve system reliability, and proactively identify and resolve incidents across enterprise environments.

The AIOps Engineer will work closely with DevOps, Site Reliability Engineering (SRE), Cloud Engineering, and Cybersecurity teams to implement intelligent monitoring, predictive analytics, and automated remediation solutions that optimize operational efficiency and reduce downtime.

Job Description

The AIOps Engineer is responsible for designing, implementing, and managing AI-driven operational solutions that automate monitoring, incident management, anomaly detection, and performance optimization. This role combines expertise in machine learning, cloud computing, data analytics, and IT operations to build intelligent systems capable of analyzing large volumes of operational data and providing actionable insights.

The position requires strong technical expertise in cloud platforms, observability tools, automation frameworks, and machine learning techniques to improve infrastructure resilience and operational performance.

Key Responsibilities

AI-Driven Monitoring & Observability

• Develop and maintain AI-powered monitoring solutions for applications, infrastructure, and cloud environments.
• Implement anomaly detection and predictive analytics models to identify operational issues before they impact users.
• Analyze logs, metrics, and events from enterprise systems to generate actionable insights.
• Build dashboards and visualizations to monitor system health and performance.

Incident Management & Automation

• Design intelligent incident detection and response workflows.
• Develop automated remediation solutions for common operational issues.
• Reduce Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR) incidents.
• Integrate AIOps platforms with IT Service Management (ITSM) systems.

Machine Learning & Data Analytics

• Build machine learning models for predictive maintenance and capacity planning.
• Analyze historical operational data to identify trends and optimize system performance.
• Develop data pipelines to process and analyze high-volume telemetry data.
• Continuously improve model accuracy and operational effectiveness.

Cloud & Infrastructure Optimization

• Monitor and optimize cloud resources and application performance.
• Implement automation solutions for cloud provisioning and configuration management.
• Improve system reliability, scalability, and availability.
• Support multi-cloud environments and distributed systems.

Collaboration & Cross-Functional Support

• Partner with DevOps, SRE, Infrastructure, and Security teams to improve operational resilience.
• Participate in major incident investigations and root cause analysis.
• Provide recommendations for performance optimization and operational improvements.
• Document AIOps architectures, workflows, and best practices.

Governance & Continuous Improvement

• Establish AIOps standards and operational procedures.
• Ensure data quality and integrity across monitoring platforms.
• Monitor the effectiveness of AI-driven operational processes.
• Identify opportunities for automation and operational efficiency improvements.

Required Qualifications

• Bachelor’s degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, or a related field.
• 3–7+ years of experience in IT Operations, DevOps, Site Reliability Engineering, Cloud Engineering, or AIOps.
• Strong understanding of machine learning concepts and operational analytics.
• Experience with cloud computing, monitoring tools, and automation technologies.
• Excellent analytical, problem-solving, and communication skills.

Preferred Qualifications

• Master’s degree in Artificial Intelligence, Data Science, Computer Science, or Information Systems.
• Experience with large-scale distributed systems and enterprise cloud environments.
• Industry certifications such as:
  • AWS Certified Solutions Architect
  • Microsoft Azure Administrator Associate
  • Google Professional Cloud Engineer
  • Certified Kubernetes Administrator (CKA)
  • ITIL Foundation Certification

Technical Skills

Programming & Scripting

• Python
• SQL
• Bash
• PowerShell

Cloud Platforms

• Amazon Web Services (AWS)
• Microsoft Azure
• Google Cloud Platform (GCP)

Monitoring & Observability Tools

• Datadog
• Dynatrace
• Splunk
• Prometheus
• Grafana
• New Relic

DevOps & Automation

• Docker
• Kubernetes
• Terraform
• Jenkins
• GitHub Actions
• CI/CD Pipelines

Machine Learning & Analytics

• Scikit-learn
• TensorFlow
• Pandas
• NumPy
• Predictive Analytics
• Anomaly Detection

Key Performance Indicators (KPIs)

• Reduction in Mean Time to Detect (MTTD)
• Reduction in Mean Time to Resolve (MTTR)
• Infrastructure Availability and Uptime
• Incident Prediction Accuracy
• Percentage of Automated Incident Resolution
• Reduction in Operational Costs
• System Performance and Reliability Improvements