AI-Ops Engineer

Job Description :

Pay Range: $55hr - $60hr

Experience Requirements:

• 5+ years in IT Operations, Data Engineering, or related fields.
• Experience in Azure Data Services, ETL/ELT processes, and ITIL-based operations.
• 2+ years in AIOps implementation, monitoring, and automation.

• Bachelor s or master s degree in computer science, Engineering, or a related field.

• Client, Power Shell, or Python, Alerts & Logs Monitoring, Confluence and Share Point

• Basic Understanding of Azure Data Services (ADF, Synapse, Databricks).
• Experience in monitoring alerts from data pipelines (Azure Data Factory, Synapse, ADLS, MS Fabric etc.)
• Familiarity with ETL/ELT concepts, data validation, and pipeline orchestration.
• Experience in identifying failures in ETL jobs, scheduled loads, and streaming data services.
• Hands-on experience with IT monitoring tools (e.g. Client, Azure Monitor, Dynatrace, or similar tools).
• Skilled in creating and updating runbooks and SOPs.
• Familiarity with data refresh cycles, batch vs. streaming differences.
• Familiarity with ITIL processes for incident, problem, and change management.
• Strong attention to detail, ability to follow SOPs, and effective communication for incident updates.
• Solid understanding of containerized services (Docker/Kubernetes) and Dev Ops pipelines (Azure Dev Ops, Git Hub Actions), always with an eye on data layer integration.
• Proficiency in Jira, Confluence and SharePoint for status updates and documentation.
• Understanding of scripting (Power Shell, Python, or Shell) for basic automation tasks.
• Ability to interpret logs and detect anomalies proactively.
• Analytical thinking for quick problem identification and escalation.

• Exposure to CI/CD for data workflows, real-time streaming (Event Hub, Kafka).
• Understanding of Data governance and compliance basics.
• Experience with anomaly detection, time-series forecasting, and log analysis.

• Monitor and support data pipelines on Azure Data Factory, Databricks, and Synapse.
• Perform incident management, root-cause analysis for L1 issues, and escalate as needed.
• Surface issues clearly & escalate to appropriate SME teams so they can be fixed at the root avoid repetitive short fixes.
• Identify whether issues are at pipeline level, data source level, or infrastructure level and route accordingly.
• Document incident resolution patterns for reuse.
• Acknowledge incidents promptly and route them to the correct team.
• Execute daily health checks, maintain logs, and update system status in collaboration tools.
• Work strictly as per SOPs documented by the team.
• Maintain and update SOPs, runbooks, and compliance documentation.
• Update system health status every 2 hours during the shift in Confluence or SharePoint.
• Update incident status every 4 hours for P1/P2 tickets.
• Complete service tasks on time as per SLA to release queues quickly.
• Ensure compliance with enterprise data security, governance, and regulatory requirements.
• Collaborate with data engineers, analysts, Dev Ops/SRE teams and business teams to ensure reliability and security.
• Implement best practices in ML operations and productionization.