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Designation AIOps Manager

Job Description

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.

Desired Profile

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.

Experience: 9 years and above

Education: B.Tech. / BS in Computer Science

Technical Skills & Certifications

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.

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