AI Engineer
Tasks
- Apply MLOps for model deployment monitoring and maintenance
- Build AI model serving infrastructure
- Build and integrate ETL pipelines
- Build proof of concepts for AI models
- Conduct technical due diligence for AI technologies
- Develop API endpoints for AI services
- Develop automated data pipelines for training and inference
- Document findings and recommend AI technology adoption
- Ensure secure connectivity for AI tools
- Implement reliable data movement code
- Implement security protocols for AI data handling
- Maintain and monitor AI model performance
- Manage cloud environments for AI workloads
- Stress test AI vendor APIs and model endpoints
Perks/Benefits
- N/A
Skills/Tech-stack
API Development | AWS | Azure | Bias detection | Computer Vision | Data Governance | Data pipeline | Data pipeline management | Docker | ETL | Explainability | GCP | Kubernetes | Language Processing | MLOps | Model Deployment | Model Interpretability | Model Monitoring | Natural Language | Natural Language Processing | Pipeline Management | Privacy Compliance | PyTorch | Python | Scikit-learn | TensorFlow
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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