What is the Certified AIOps Engineer?

The Certified AIOps Engineer credential is designed for practitioners who build, deploy, and maintain AIOps solutions in production environments. Moving beyond foundational theory, this certification focuses on the practical engineering skills required to implement intelligent monitoring, anomaly detection, and automated incident response.

Candidates will demonstrate proficiency in constructing data ingestion pipelines, configuring anomaly detection models, integrating AIOps tooling with CI/CD workflows, and building auto-remediation runbooks. This is the certification that proves you can turn AIOps concepts into working systems.

Certification at a Glance

  • Level: Mid-Level / Practitioner
  • Exam Duration: 120 Minutes
  • Questions: 75 MCQs + Practical Scenarios
  • Passing Score: 72%
  • Validity: 3 Years (Renewable)
  • Price: $499

What You Will Learn

Develop the engineering skills to implement AIOps in real-world environments

AIOps Toolchain Mastery

Evaluate, configure, and operate leading AIOps platforms including observability stacks, log aggregators, and ML-powered alerting tools.

Data Pipeline Engineering

Build robust data ingestion pipelines that collect, normalize, and route logs, metrics, and traces from heterogeneous infrastructure.

Anomaly Detection Implementation

Implement statistical and ML-based anomaly detection across time-series metrics, application logs, and infrastructure telemetry.

Auto-Remediation

Design and deploy automated remediation workflows using runbook automation, event-driven triggers, and feedback loops.

CI/CD Integration

Integrate AIOps monitoring and quality gates into continuous integration and delivery pipelines for deployment intelligence.

Capstone Project

Complete an end-to-end capstone project that integrates monitoring, detection, and remediation into a functioning AIOps pipeline.

Who Should Enroll?

Built for engineers who implement and operate AIOps systems daily

DevOps / SRE Engineers

Site reliability and DevOps engineers seeking to augment their monitoring and incident response capabilities with AI.

IT Automation Specialists

Professionals who work on infrastructure automation and want to add intelligent decision-making to their workflows.

Platform Engineers

Engineers building internal developer platforms who want to embed observability and intelligence into their tooling.

Course Modules

Six comprehensive modules covering the full AIOps engineering lifecycle

Survey the AIOps tool landscape including observability platforms, log management systems, APM solutions, and ML engines. Learn to evaluate tools against organizational requirements, configure integrations, and build a cohesive monitoring stack.

Design and implement data collection pipelines for logs, metrics, and traces. Cover data normalization, enrichment, routing, and storage strategies. Work with streaming frameworks and batch processing for operational data.

Implement anomaly detection using statistical methods, isolation forests, autoencoders, and time-series models. Configure dynamic thresholds, seasonal baselines, and multi-variate correlation for accurate alerting.

Build automated remediation workflows that respond to detected anomalies. Implement runbook automation, event-driven triggers, approval gates, and closed-loop feedback systems that improve over time.

Embed AIOps intelligence into CI/CD pipelines. Implement deployment quality gates, canary analysis, automated rollback triggers, and post-deployment anomaly monitoring to achieve deployment confidence.

Apply all learned skills in an end-to-end project. Build a complete AIOps pipeline from data ingestion through anomaly detection to automated remediation, demonstrating production-ready engineering practices.

Exam & Certification Details

A rigorous exam that validates real-world engineering competence

Exam Format

75 MCQs + Practical Scenarios

Duration

120 Minutes

Passing Score

72%

Delivery

Online Proctored Exam

Validity

3 Years (Renewable)

Prerequisites

AIOps Foundation recommended

Career Opportunities

Engineering roles that demand hands-on AIOps expertise

Job Roles
  • AIOps Engineer
  • Site Reliability Engineer
  • Observability Engineer
  • Platform Engineer
  • Automation Engineer
Salary Range
  • Mid Level: $90,000 - $120,000
  • Senior Level: $120,000 - $155,000
  • Premium compensation at top-tier tech companies and consulting firms.
Industries
  • Cloud Service Providers
  • FinTech & Banking
  • Managed Service Providers
  • Enterprise Software
  • Telecommunications

Certification Benefits

Tangible advantages that accelerate your engineering career

Digital Badge

Earn a verifiable credential to display on LinkedIn, GitHub, and professional profiles.

Engineer Community

Access a private Slack channel with fellow certified engineers for knowledge sharing and support.

Upgrade Discounts

Receive discounts when advancing to the Professional or Architect certifications.

Lab Environment

Extended access to hands-on lab environments for continued practice after certification.

Certification Pricing

Certified AIOps Engineer

Complete certification package

$499
  • Full course materials & engineering guides
  • Hands-on lab environment access
  • Practice exams with scenario questions
  • One exam attempt included
  • Digital badge & certificate
  • Capstone project review
Enroll Now

Frequently Asked Questions

The Foundation certification is strongly recommended but not strictly required. Candidates should have a solid understanding of AIOps fundamentals and at least one year of experience in IT operations, DevOps, or SRE roles.

Practical scenarios present real-world situations such as configuring an anomaly detection model, troubleshooting a broken data pipeline, or designing a remediation workflow. You select the best approach from multiple options based on engineering best practices.

You can renew by passing an updated exam or by earning a higher-level certification (Professional or Architect) before your current credential expires. Continuing education credits are also accepted.

Labs cover industry-standard tools including Prometheus, Grafana, Elasticsearch, and open-source ML frameworks. The focus is on transferable skills rather than vendor-specific training.

Basic scripting knowledge (Python or Bash) is recommended since the certification involves hands-on engineering tasks. However, the course provides guided examples and templates to help you build the necessary skills.

Ready to Get Certified?

Start your certification journey today.