Job Summary:
The AIOps Engineer will play a key role in implementing and managing intelligent operations platforms that enhance observability, automate incident response, and support root cause analysis across complex systems. This individual will design and optimize AI-driven monitoring and alerting pipelines, integrate anomaly detection frameworks, and lead automation and security enhancements across hybrid cloud environments. The engineer will work with self-hosted LLMs and ML pipelines using tools such as Kubeflow, rayserve, kserve, and Kubernetes orchestration. Additionally, the role involves strengthening delivery across AI-powered infrastructure and applications, leveraging expertise in cloud platforms (AWS, Azure, GCP), Kubernetes, Kong, artifactory, and on-premise banking systems.
Roles and responsibilities:
Implement and manage AIOps platforms for intelligent monitoring, alerting, anomaly detection, and root cause analysis (RCA).
Possess end-to-end knowledge of VLLM model hosting and inferencing.
Advanced knowledge of public cloud platforms such as AWS and Azure.
Build and maintain machine learning pipelines and models for predictive maintenance, anomaly detection, and noise reduction.
Experience in production support and real-time issue handling.
Design dashboards and visualizations to provide operational insights to stakeholders.
Working knowledge of Bedrock, SageMaker, EKS, Lambda, etc.
1 to 2 years of experience with Jenkins and GoCD to make build/deploy pipelines.
Hands-on experience with open-source and self-hosted model APIs using SDKs.
Drive data-driven decisions by analyzing operational data and generating reports on system health, performance, and availability.
Basic knowledge of kserve and rayserve inferencing .
Good knowledge of high level scaling using Karpenter , Keda , System based vertical/horizontal scaling.
Strong knowledge on linux operating system or linux certified .
Previous experience with Helm chart deployments and Terraform template and module creation is highly recommended.

Key Success Metrics
Contribute to the deployment of GenAI solutions across cloud and on-premises environments. Support timely deliverables by collaborating with cross-functional teams and assist in troubleshooting processes.

AI Ops - Engineer