About this role
About the Role We are seeking a technically strong AIOps Engineer with hands-on expertise in programming, workflow automation, and integration of AI-driven insights into IT operations. The candidate should be able to design and deploy automation pipelines that enhance observability, predictive incident management, and self-healing systems. What You'll Do
- Design and deploy automation pipelines to improve observability, incident management, and self-healing capabilities.
- Develop automation scripts in Python for data processing and integration; build enterprise-grade backend automation in Java (Spring/Maven); create dashboards/UI with JavaScript/TypeScript (Node.js, Angular).
- Leverage GitHub Workflows and Copilot to automate pipeline creation and code generation.
- Integrate AI/ML models into operational workflows using Azure AI Studio.
- Work with observability concepts: anomaly detection, root-cause analysis, log/event correlation, and observability pipelines (metrics, logs, traces).
- Collaborate across teams to deploy microservices on cloud platforms (Azure/AWS/GCP) and monitor via modern observability tools. What We're Looking For
- 5–6 years of hands-on experience in AIOps or similar roles.
- Strong coding experience in Python for automation and data processing; Java (Spring/Maven) for backend automation; JavaScript/TypeScript with Node.js/Angular for dashboards.
- Experience with GitHub Workflows/Copilot for automated pipelines and code generation.
- Familiarity with Azure AI Studio and integrating AI/ML models into operational workflows.
- Knowledge of AIOps concepts: anomaly detection, root cause analysis, log/event correlation; observability pipelines (metrics, logs, traces). Nice to Have
- Experience with ML frameworks: PyTorch, TensorFlow, Scikit-learn.
- Hands-on with monitoring/observability tools: Splunk, Dynatrace, Datadog, New Relic.
- Knowledge of cloud-native platforms (Azure, AWS, GCP) and microservices deployments.