ML Ops Engineer
Tasks
- Architect ML ops pipelines
- Automate AI ML governance
- Build and maintain RESTful APIs
- Collaborate with data scientists and product owners
- Debug and troubleshoot ML workloads
- Deploy and monitor machine learning models
- Design and implement ETL data pipelines
- Develop end-to-end AI products
- Guide AI/ML solution development
- Implement CI/CD workflows
- Monitor optimize and maintain ML solutions
- Orchestrate MLOps workflows on cloud
- Use GPU and CPU resources for ML workloads
- Write production ready scripts
Perks/Benefits
- Diversity & Inclusion programs
- Employee share option plan
- Flexible work environment
- Meal tickets
- Medical services
- Training programs
Skills/Tech-stack
Apache Airflow | CI/CD | Cloud platform | Data Pipelines | Deep learning | Deep learning frameworks | Docker | ETL | GPU Computing | Git | Google Cloud | Google Cloud Platform | Kubeflow | Kubernetes | Learning frameworks | Machine Learning | Microsoft Azure | Model Monitoring | Python | REST APIs | Unix/Linux
Education
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