Jobs → Engineering → AIOps/MLOps Engineer

About the Role We are seeking an experienced AIOps/MLOps Engineer to build and manage production-ready machine learning pipelines and intelligent operations workflows. This role requires strong expertise in Python, ML lifecycle automation, Kubernetes, CI/CD, model monitoring, and observability. Key Responsibilities • Design and maintain end-to-end ML pipelines for training, validation, deployment, and rollback • Automate model versioning, artifact management, and reproducible releases • Monitor model performance, drift, data quality, latency, and service health • Implement AIOps capabilities for anomaly detection, event correlation, and alert noise reduction • Integrate ML workloads with CI/CD pipelines and containerized environments • Collaborate with data scientists and platform teams to productionize ML models • Implement model governance, lineage, access controls, and deployment approvals • Optimize model-serving reliability, scalability, and resource utilization • Troubleshoot pipeline failures and production incidents • Develop reusable automation standards, technical documentation, and runbooks Requirements • 9+ years of professional experience • Strong Python and automation skills • Hands-on experience with MLOps pipelines and ML lifecycle management • Experience with CI/CD tools such as Jenkins, GitHub Actions, or GitLab CI • Knowledge of Docker and Kubernetes • Experience with model serving, batch and real-time inference, and deployment strategies • Strong understanding of observability, metrics, logs, traces, and alerting • Experience with AIOps, anomaly detection, or event correlation • Knowledge of Terraform, Ansible, or other Infrastructure as Code tools • Strong troubleshooting and incident management skills