ML Ops Developer

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).· Security, governance, and access controls: authentication/authorization, encryption, policy/guardrails, and compliance\-focused logging/traceability for production ML.· Cross\-functional collaboration with data scientists, engineers, and platform teams to productionize models following best practices for repeatability, standardization, and operational efficiency.