Data Engineer / MLOps and AI Engineer
Tasks
- Apply secrets management role based access control and network policies
- Build batch and streaming data workflows in Snowflake
- Build prompt architecture and agent memories
- Build testing frameworks for ML and agent generated code
- Create validation pipelines with quality gates
- Define and manage feature sets lineage and reuse
- Design agent executable workflows on Cortex AI
- Develop ML pipelines for experimentation to production
- Enforce model agent and data governance with audit trails
- Engineer instruction patterns and acceptance criteria
- Ensure HIPAA PHI GDPR FDA and REMS compliance
- Implement CI/CD, automated testing, and deployment
- Implement anomaly detection and hypothesis generation for commercial data
- Manage documentation and release runbooks rollback and change tickets
- Monitor model performance data drift bias and service health
- Optimize token usage and agent execution cost
- Package train and deploy ML models
- Set up logging tracing and alerting for ML systems
Perks/Benefits
Skills/Tech-stack
21 CFR | 21 CFR Part 11 | Access Control | Alerting | Apache Airflow | Azure DevOps | Bias detection | CFR Part 11 | CI/CD | Container Services | Contract testing | Cortex AI | Data Drift | Databricks MLFlow | Docker | Experiment tracking | GDPR | GitHub Actions | Go | HIPAA | Integration Testing | JavaScript | LLM | Linting | Logging | Model Governance | Model Registry | Monitoring | Part 11 | Prompt engineering | Python | Role-Based Access | Role-Based Access Control | Rust | SOC2 | SQL | SageMaker | Secrets management | Snowflake | Snowpark | Snowpark Container Services | Spark | Test-Driven | Test-Driven Development | Token Management | Tracing | TypeScript | Unit Testing
Education
N/A
Roles
AI | AI Engineer | Data Engineer | Engineer | Learning Engineer | MLOps Engineer | Machine Learning Engineer
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