Senior DevOps Engineer

Key Responsibilities:

Elastic Stack Architecture & Deployment

• Design and implement Elastic Stack solutions (Elasticsearch, Logstash, Kibana, Beats) for both cloud and on-premises environments.
• Architect and manage Elastic clusters , including:
  • Cluster types (hot-warm-cold-frozen architecture)
  • Shard allocation and rebalancing
  • Index Lifecycle Management (ILM)
  • Snapshot and restore strategies
  • Cross-cluster search and replication
• Configure and fine-tune Logstash servers , including:
  • JVM heap size optimization
  • Pipeline tuning and throughput optimization
  • Persistent queues and dead letter queues
• Implement security best practices using Elastic Security features (TLS, RBAC, API keys).

Kibana Dashboards & Alerting

• Build and maintain Kibana dashboards for real-time monitoring and analytics.
• Define alerting rules using Watcher or Kibana alerting framework.

Elastic AI/ML & AIOps Use Cases

• Predictive IT Maintenance : Use ML jobs to detect anomalies and forecast incidents.
• Root Cause Analysis : Correlate logs and metrics to identify root causes.
• Intelligent Escalation : Trigger alerts with probable causes and remediation steps.
• Automated Remediation : Integrate with ITSM tools for ticketing and automated fixes.
• Proactive Capacity Resizing : Use time-series forecasting to predict and scale resources.
• Event Correlation & Noise Reduction : Group related events to reduce alert fatigue.
• Adaptive Baselining & Thresholding : Automatically generate baselines and thresholds.

Required Skills & Qualifications:

• Bachelor’s degree in Computer Science, Information Technology, or related field.
• Strong hands-on experience with Elastic Stack (Elasticsearch, Logstash, Kibana, Beats).
• Deep understanding of:
  • Elastic cluster architecture
  • Sharding and replication
  • Index Lifecycle Management (ILM)
  • Hot, warm, cold, and frozen node tiers
  • Snapshot and restore
  • Elastic Security and role-based access control
• Experience with Logstash tuning (JVM heap, pipeline workers, persistent queues).
• Proficiency in Grok , Ruby , and data transformation techniques.
• Familiarity with DevOps tools (CI/CD, Git, Docker, Kubernetes).
• Experience with Elastic ML features or similar ML tools for observability.

Preferred / Added Advantages:

• Participation in data analytics-related innovation programs , internships , or competitions .
• Experience with cloud platforms (AWS, Azure, GCP) and Elastic Cloud.
• Exposure to ITSM tools (e.g., ServiceNow, Jira Service Management).
• Elastic certifications (e.g., Elastic Certified Engineer) are a plus.