Data Engineer / MLOps and AI Engineer
Tasks
- Apply role based access control and secrets management
- Build agent workflows on Cortex AI
- Build data pipelines in Snowflake
- Build testing frameworks for ML and agent code
- Coordinate releases with operations and IT
- Create logging tracing and alerting for ML systems
- Create validation pipelines with automated quality gates
- Design prompt architecture and prompt engineering
- Develop batch and streaming workflows
- Develop documentation runbooks and release management
- Enforce governance security and compliance for models and data
- Implement CI/CD for ML pipelines
- Implement instruction architecture for agents
- Implement version control audit trails and approvals
- Manage machine learning model lifecycle to production
- Optimize token economics and cost for agents
- Provide model serving and deployment
- Set up monitoring for model performance and data drift
Perks/Benefits
Skills/Tech-stack
21 CFR | 21 CFR Part 11 | Access Control | Airflow | Amazon SageMaker | Azure DevOps | CFR Part 11 | CI/CD | Contract testing | Data Privacy | Databricks MLFlow | Docker | Experiment tracking | GDPR | GitHub Actions | Go | HIPAA | Healthcare Data | Healthcare data privacy | Integration Testing | JavaScript | Linting | Machine Learning | Machine learning lifecycle | Model Registry | Model Serving | Part 11 | Prompt engineering | Python | Role-Based Access | Role-Based Access Control | Rust | SOC 2 | SQL | Secrets management | Snowflake | Snowpark | Spark | TDD | Test-Driven | Test-Driven Development | Token Economics | Token optimization | TypeScript | Unit Testing
Education
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