Machine Learning Engineer, AI Solutions Hub (AISH)
Tasks
- Address prompt injection and data leakage risks
- Apply data governance and privacy controls
- Automate model artifact testing and data validation
- Build machine learning deployment pipelines
- Debug production model issues
- Implement batch and real-time inference
- Implement infrastructure-as-code
- Implement logging, monitoring, and alerting
- Manage model registry and promotion workflows
- Package and serve ML models
- Secure credentials and sensitive data
- Set up CI CD for ML applications
- Validate models and support A B testing
Perks/Benefits
- Commuting and transportation
- Dental insurance
- Medical insurance
- Paid time off
- Retirement benefits
- Tuition assistance
- Vision insurance
- Wellness programs
Skills/Tech-stack
A/B | A/B Testing | AWS | Agile | Alerting | Azure | B testing | Batch inference | CI/CD | Cloud platform | Code review | DVC | Data Governance | Data Versioning | Deep learning | Docker | Docker containers | FastAPI | GRPC | Generative AI | Git | Google Cloud | Google Cloud Platform | Inference | Kubernetes | LLM Deployment | Language Models | Large Language Models | Logging | MLOps | MLflow | Machine Learning | Model Packaging | Model Registry | Model Serving | Monitoring | Prompt engineering | Python | REST | Real Time | Real-time Inference | Terraform | Testing | Version control | Weights and Biases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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