Role overview
- Impact: You will see your work directly influence how models perform in the real world
- Growth: You’ll be mentored by senior engineers in one of the fastest-growing niches in tech
- Innovation: We encourage experimenting with new tools to solve the "unsolved" problems of AI reliability
What you'll work on
- Pipeline Automation: Assist in building and maintaining CI/CD pipelines specifically for machine learning (CT - Continuous Training)
- Model Deployment: Package ML models into reproducible environments using Docker and deploy them via REST APIs or batch processing
- Monitoring & Logging: Help set up dashboards to track model performance, data drift, and system health
- Infrastructure as Code: Work with senior engineers to manage cloud resources (AWS/GCP/Azure) using tools like Terraform or CloudFormation
- Collaboration: Bridge the gap between Data Scientists (who build the models) and Software Engineers (who build the product) to ensure seamless integration
What we're looking for
- Experience with MLOps tools like MLflow, Kubeflow, or DVC
- Exposure to cloud platforms (AWS SageMaker, Google Vertex AI, or Azure ML)
- Basic understanding of Kubernetes or orchestration tools
- Knowledge of SQL and NoSQL databases
Tags & focus areas
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