Tech S and T-AI Ops Engineer-Senior-GDSN02

• Architect and implement enterprise-grade AIOps solutions to automate incident detection, root cause analysis, and remediation across cloud and hybrid environments.
• Lead the integration of telemetry data from tools like Prometheus, Grafana, AppDynamics, Dynatrace, and Azure Monitor into centralized AIOps platforms for unified observability and intelligent event correlation.
• Design and maintain ML models in Python for anomaly detection, predictive analytics, and operational forecasting, ensuring scalability and accuracy.
• Build and optimize real-time and batch data pipelines using Apache Kafka, Logstash, and Fluentd to process logs, metrics, and traces from distributed systems.
• Collaborate with DevOps, SRE, and platform engineering teams to embed AIOps capabilities into CI/CD workflows and infrastructure-as-code practices.
• Drive automation of operational tasks and remediation workflows using Python, Azure Functions, and orchestration tools to enable self-healing systems.
• Develop dashboards and visualizations using Grafana, Kibana, or Power BI to deliver actionable insights to engineering, operations, and business teams.
• Implement alert noise reduction strategies using ML-based filtering, deduplication, and suppression techniques to improve signal-to-noise ratio.
• Ensure compliance with security, governance, and audit policies, embedding DevSecOps principles and aligning with regulatory standards.
• Lead technical evaluations of AIOps platforms and tools, making recommendations for adoption based on business needs and operational maturity.
• Manage and mentor a team of AIOps engineers, fostering a culture of innovation, continuous learning, and operational excellence.
• Partner with business stakeholders to identify opportunities for cost optimization, risk reduction, and service reliability improvements through AIOps.
• Contribute to strategic roadmaps, budget planning, and vendor assessments, aligning AIOps initiatives with broader IT and business goals.
• Stay current with emerging trends in AIOps, observability, cloud-native operations, and AI-driven automation, and drive their adoption within the organization.

• 9+ years of experience in IT operations, DevOps, or SRE, with at least 3 years in AIOps or AI/ML-driven automation.
• Proven experience in technical leadership, team management, and cross-functional collaboration.
• Deep expertise in AIOps platforms: Moogsoft, BigPanda, Splunk ITSI, ServiceNow ITOM, or custom ML-based solutions.
• Strong proficiency in Python, with working knowledge of SQL, PowerShell, and Bash.
• Hands-on experience with Azure, AWS, or GCP, including monitoring and automation services.
• Skilled in CI/CD tools (Azure DevOps, GitHub Actions, Jenkins), IaC (Terraform, Ansible), and Kubernetes.
• Familiarity with observability stacks (ELK, OpenTelemetry, Kafka) and data engineering workflows.
• Excellent communication, stakeholder management, and problem-solving skills.

9 years and above

B.Tech. / BS in Computer Science

• Certified in cloud platforms: Azure Data Engineer Associate, AWS Data Analytics, or Google Data Engineer.
• Proficient in Python, SQL, and Bash for automation and data workflows.
• Experience with Apache Kafka, Spark, AWS Glue, and ETL tools like NiFi, Talend, or ADF.
• Skilled in managing data on Azure Data Lake, Amazon Redshift, or BigQuery.
• Familiar with CI/CD, Terraform, Docker, Kubernetes, and monitoring tools like Prometheus and Grafana.
• Strong understanding of DevSecOps, data governance, and compliance frameworks. Understanding of monitoring tools (Prometheus, Grafana, ELK) and DevSecOps principles.