Role - MLOps with LLM

Experience - 4 to 15 years

Interview Location - Bengaluru

Location - PAN INDIA

Job description

• Strong knowledge of MLOps principles and the end-to-end ML lifecycle: data preparation → training → validation → deployment/serving → monitoring/refresh pipelines.
• Design and implement CI/CD (and CT/continuous training) pipelines for ML workflows, including testing, promotion, rollback, and reproducible builds.
• Hands-on with containerization and orchestration (e.G., Docker/Kubernetes) and ML pipeline tooling such as MLflow/Kubeflow (or equivalent).
• Monitoring & observability for ML systems: service + data + model health tracking, drift checks (feature/target/concept), alerts/triggers, and root-cause analysis.
• Cloud platform experience (AWS/Azure/GCP) to deploy and run ML workloads using managed services and cloud-native components (e.G., GKE, BigQuery, Cloud Storage, Vertex AI capabilities...