Tech Stack

Description

You will build and maintain robust CI/CD pipelines for ML models, automate the end-to-end lifecycle of models, and develop infrastructure for scalable training and serving. You'll monitor model performance in production, detect drift, and collaborate with data scientists and DevOps to operationalize ML models into products that protect critical infrastructure.

Requirements

• 3+ years experience in MLOps, DevOps, or ML Engineering
• Strong programming skills in Python and Bash
• Experience with ML frameworks for training/serving (e.g., Ray, SageMaker jobs)
• Hands-on experience with CI/CD tools (e.g., GitHub Actions, Bitbucket pipelines, TeamCity)
• Proficiency with containerization and orchestration (Docker, Kubernetes)

Responsibilities

• Build and maintain CI/CD pipelines for ML models and data workflows
• Automate end-to-end lifecycle of ML models from training to deployment and monitoring
• Develop infrastructure for scalable, reproducible model training and serving in Cloud or on-premise
• Monitor model performance, detect model/data drift, and implement alerting systems
• Collaborate with Data Scientists and DevOps to operationalize ML models and integrate into products