Overview

We are looking for an experienced AIOps Engineer with expertise in Google Cloud Platform (GCP), AIOps/MLOps/LLMOps, intelligent observability and monitoring, CI/CD, Python, Pub/Sub/Kafka, distributed computing, GitHub, data pipelines, and GCP services such as Vertex AI, BigQuery, Dataflow, GKE, and Cloud Operations Suite (Cloud Monitoring, Cloud Logging, Cloud Trace).

Responsibilities

• Design, develop, and maintain AIOps pipelines on GCP for ingesting, processing, and analyzing telemetry data (logs, metrics, traces, events) to enable intelligent monitoring and observability.
• Build and deploy ML models for anomaly detection, event correlation, root-cause analysis, and predictive incident management using Vertex AI and BigQuery ML.
• Collaborate with data scientists, SREs, software engineers, and DevOps teams to embed AI-driven automation into IT operations using best practices in AIOps and MLOps.
• Automate end-to-end ML and operations workflows, including data preprocessing, model training, evaluation, deployment, and automated remediation, using tools like Vertex AI Pipelines, Kubeflow, or Cloud Composer (Apache Airflow).
• Implement CI/CD pipelines using Cloud Build, GitHub Actions, and Terraform (IaC) for automated deployment, testing, and monitoring of models and services on GCP.
• Utilize Pub/Sub, Dataflow, and Kafka for real-time telemetry ingestion, streaming analytics, and event-driven automation.
• Optimize GCP infrastructure (GKE, Compute Engine, BigQuery, Cloud Storage) for scalability, performance, reliability, and cost efficiency.
• Manage GitHub repositories for version control and collaboration on AIOps and machine learning projects.
• Integrate with ITSM/ITOM platforms (e.g., ServiceNow) and various data connectors to access and process operational data from different sources.
• Develop and maintain documentation for AIOps pipelines, GCP infrastructure, runbooks, and processes.
• Stay up to date on emerging technologies and best practices in AIOps, SRE, machine learning operations, and cloud engineering.

Qualifications

• 5+ Years of prior experience in DevOps/SRE/Data Engineering, with strong exposure to AIOps and MLOps.
• 3+ Years of hands-on experience with GCP services (Vertex AI, BigQuery, Dataflow, Pub/Sub, GKE, Cloud Operations Suite) in production environments.
• Strong proficiency in Python programming language.
• Experience with observability and monitoring tools (Cloud Monitoring, Prometheus, Grafana, ELK/Splunk, Datadog, or similar) and distributed computing frameworks.
• Hands-on experience with CI/CD pipelines, Terraform/IaC, Kubernetes (GKE), and automation tools.
• Exposure in deploying a use case in production leveraging Generative AI involving prompt engineering and RAG Framework (e.g., LLM-assisted incident summarization or intelligent ticket triage).
• Familiarity with Pub/Sub, Kafka, or similar messaging systems.
• Strong problem-solving skills and the ability to iterate and experiment to optimize AI model behavior and operational automation.
• Excellent problem-solving skills and attention to detail.
• Ability to communicate effectively with diverse clients/stakeholders.

Education

• Bachelor’s or master’s degree in computer science, Engineering, or a related field.
• Tier I/II candidates preferred.