AIOps Engineer

About the Role

Key Responsibilities

• Apply machine learning algorithms to existing operational data (logs, metrics, events) to predict system failures and proactively address potential incidents.
• Implement automation for routine DevOps practices including automated scaling, resource optimization, and controlled restarts.
• Develop and maintain self-healing systems to reduce manual intervention and enhance system reliability.
• Build anomaly detection models to quickly identify and address unusual operational patterns.
• Collaborate closely with SREs, developers, and infrastructure teams to continuously enhance the operational stability and performance of the system.
• Provide insights and improvements through visualizations and reports leveraging AI-driven analytics.
• Create a phased roadmap to incrementally enhance operational capabilities and align with strategic business goals.

Requirements

• Strong experience with AI/ML frameworks and tools (e.g., TensorFlow, PyTorch, scikit-learn).
• Proficiency in data processing and analytics tools (e.g., Splunk, Prometheus, Grafana, ELK stack).
• Solid background in scripting and automation (Python, Bash, Ansible, etc.).
• Experience with cloud environments and infrastructure automation.
• Proven track record in implementing proactive monitoring, anomaly detection, and self-healing techniques.
• Excellent analytical, problem-solving, and strategic planning skills.
• Strong communication skills and the ability to effectively collaborate across teams.

Nice to Have

• Background in DevOps/Site Reliability Engineering.
• Familiarity with containerization and orchestration platforms (Kubernetes, Docker).
• Experience in building scalable, distributed systems.