Requirements

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.

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

Job 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.

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