Description

Own the end-to-end ML lifecycle, from data preparation and training to deployment, serving, and monitoring.

This role is on-site.

Responsibilities

• 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 tooling like MLflow and Kubeflow.
• Deploy and manage ML workloads on AWS, Azure, and GCP, ensuring security, governance, and access controls.
• Implement monitoring and observability for service, data, and model health, detecting drift and driving root-cause analysis.

Required Skills

• 5+ years of relevant experience, including 2+ years in AI/GenAI.
• Deep understanding of MLOps principles and end-to-end ML lifecycle ownership.
• Hands-on experience with Docker, Kubernetes, and CI/CD for ML workflows.
• Proficiency with ML pipeline tools such as MLflow or Kubeflow.
• Experience with cloud platforms (AWS, Azure, or GCP) and their managed ML services.
• Knowledge of monitoring/observability, including drift detection and production alerting.
• Bachelor's degree in Computer Science, Artificial Intelligence, or IT.

Preferred Skills

• 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, and advanced root-cause analysis.

Key Skills

Education

Any Graduate