Chief Operations Intelligence Specialist

2 days ago

Mumbai, Maharashtra, India beBeeAIOps Full time ₹ 15,00,000 - ₹ 25,00,000
Operational Intelligence Engineer

We are seeking a skilled professional to bridge the gap between DevOps and AI, ensuring our systems remain intelligent, automated, and running at peak performance.

Key Responsibilities:

  • Implement and manage operational intelligence platforms for intelligent monitoring, alerting, and incident response.
  • Integrate AI-driven insights into DevOps workflows to reduce downtime and improve system reliability.
  • Automate operational tasks using scripts, pipelines, and orchestration tools.
  • Build and maintain observability dashboards, leveraging metrics, logs, and traces.
  • Collaborate with data, infrastructure, and application teams to ensure seamless adoption.
  • Continuously optimize monitoring rules and anomaly detection models for evolving system needs.

Requirements:

  • Proven experience in DevOps and AI.
  • Strong knowledge of cloud platforms (AWS, Azure, GCP) and container orchestration (Kubernetes, Docker).
  • Familiarity with observability tools (Prometheus, Grafana, ELK, Splunk, Datadog).
  • Scripting/programming skills in Python, Shell, or similar.
  • Understanding of machine learning for operations anomaly detection, predictive maintenance, etc.

Technical Skills:

  • AIOps and DevOps Expertise: Experience implementing and managing AIOps platforms for intelligent monitoring, alerting, and automation.
  • Cloud Proficiency: Hands-on with AWS, Azure, or GCP for infrastructure and deployment.
  • Containerization and Orchestration: Strong in Docker and Kubernetes for scalable deployments.
  • Observability Tools: Proficiency with Prometheus, Grafana, ELK/EFK Stack, Splunk, or Datadog.
  • Automation and Scripting: Skilled in Python, Shell, or similar scripting for automating operational workflows.
  • Monitoring and Incident Response: Setting up intelligent alerts, root-cause analysis, and predictive monitoring.

Nice-to-Have:

  • GenAI Integration: Applying generative AI for operational insights or incident response.
  • MLOps Knowledge: Managing ML model lifecycles (CI/CD for ML, model monitoring).
  • Big Data and Streaming Tools: Familiarity with Kafka, Spark for operational data handling.
  • Incident Management Platforms: PagerDuty, Opsgenie, ServiceNow integrations.
  • Security Automation: Integrating security monitoring into AIOps workflows.

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