Description

You will build and own the ML operations infrastructure from concept to production.

Responsibilities

• Design and implement multi-tenant ML model serving infrastructure supporting customer isolation, monitoring, and cost allocation.
• Build CI/CD pipelines automating model training, testing, validation, and deployment.
• Establish data quality frameworks, including drift detection and monitoring at scale.
• Create model versioning, A/B testing, and rollback capabilities for production.
• Implement observability and monitoring systems for model performance, data quality, and infrastructure health.

Required Skills

• 8+ years of experience in MLOps, DevOps, or ML Infrastructure engineering.
• Proven experience architecting and building ML platforms from scratch (0→1).
• Deep understanding of multi-tenant architecture patterns (data isolation, security, cost optimization).
• Strong experience with containerization (Docker, Kubernetes) for ML workloads.
• Hands-on experience with AWS, Azure, or GCP for production ML deployment.
• Experience designing and implementing CI/CD pipelines for ML models.
• Strong knowledge of data quality monitoring and model drift detection.
• Proficiency in Python and infrastructure-as-code tools (Terraform).

Key Skills

Education

Any Gradute