Position: MLOps / DevOps for Machine Learning
Company: blackthorn.ai
Category: DevOps
Experience required: 2y
Published: 2021-08-01T00:00:00+03:00

**Role Description**
MLOps empowers data scientists and app developers to help bring ML models to production. MLOps enables you to track / version / audit / certify / re-use every asset in your ML lifecycle and provides orchestration services to streamline managing this lifecycle.

MLOps or experienced DevOps with on-premises and multi-cloud experience (at least, AWS and Azure) would need to have at least 2 years of experience. You will be responsible for the operationalization of ML models and related tasks.

**Responsibilities**
- Machine Learning process end-to-end support and automation
- Manage security and levels of permissions 
- Automate and improve development and release processes
- Automated data ingestion, feature engineering, and selection
- Model training and evaluation automation
- Model deployment and productionalization
- Model performance monitoring and feedback loop
- Automated model fine-tuning, retraining, and testing
- Data, model and process security, governance, and audit
- Federated and/or parallel ML model training

**Required Skills**
- 3+ years of hands-on experience in on-premise and cloud (preferably Azure) database design and maintenance.
- Source control: git, Bitbucket, GitLab
- Continuous Integration: Jenkins, Bamboo, TeamCity, or similar
- Deployment Automation: Jenkins, Terraform, CloudFormation
- Configuration Management: Puppet, Ansible, Chef
- Containers: Docker
- Orchestration: Kubernetes, Swarm, Mesos
- Cloud platforms: AWS, GCP, Azure

**Desired Skills**
- AWS: CloudFormation, SageMaker, Redshift
- Azure: Azure Machine Learning, Data Factory, DevOps, Event Grid, ML CLI
- Healthcare domain knowledge
- AI/ML lifecycle understanding

**Personality**
Attentive to details
Results-driven
Responsible
Proactive

**Salary**
Hourly-based or monthly-based

**Other Details**
Full remote
Flexible schedule
Small (yet professional) team
Full time or part-time (min 20 hours per week)