Walk-in || ML OPS Engineer

Role - ML Ops Developer

Experience - 4 to 15 years

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.
  Proficiency in programming languages such as Python, .Net or Java, with experience in relevant libraries and frameworks (e.g., TensorFlow, PyTorch, Keras).