Description

Key Skills: MLOps, AI/ML, ML Engineer, AWS, Azure, GCP, CICD, Kubernetes, Docker

Roles and Responsibilities:

• Own the end-to-end ML lifecycle: data preparation, training, validation, deployment/serving, and monitoring/refresh pipelines.
• Design and implement CI/CD pipelines for ML workflows, including testing, promotion, rollback, and reproducible builds.
• Build and operate containerized ML services using Docker and Kubernetes, integrating ML pipeline tooling such as MLflow and Kubeflow (or equivalents).
• Implement monitoring and observability for service, data, and model health; detect drift (feature/target/concept) and drive alerting and root-cause analysis.
• Deploy and run ML workloads on cloud platforms (AWS/Azure/GCP) using managed services and cloud-native components; ensure security, governance, and access controls for production.

Skills Required:

• 4 - 12 years of relevant experience; 2+ years in AI/GenAI is required.
• MLOps principles and end-to-end ML lifecycle ownership (data preparation through monitoring/refresh).
• CI/CD for ML workflows, including testing, promotion, rollback, and reproducible builds; experience with continuous training concepts.
• Hands-on containerization and orchestration with Docker and Kubernetes; ML pipeline tooling such as MLflow and Kubeflow (or equivalent).
• Monitoring/observability for ML systems including drift checks and production alerting; security and governance (authentication/authorization, encryption, access control).

Good to Have:

• Experience deploying on GKE, BigQuery, Cloud Storage, and Vertex AI; stronger experience with LLM/GenAI operations (2+ years).
• Experience with policy/guardrails, compliance-focused logging/traceability, and advanced root-cause analysis for ML incidents.

Education: Bachelor's degree in Computer Science, Artificial Intelligence, or IT

Key Skills

Education

Any Graduate