AI Engineer (Data and Analytics Department)
Tasks
- Build and maintain MLOps pipelines
- Design test and implement AI ML lifecycle
- Develop AI/ML models
- Implement automated retraining infrastructure
- Lead stakeholder engagement and technical leadership
- Manage code to model retirement lifecycle
- Monitor model performance and data drift
- Optimize AI models with quantization pruning and distillation
- Set up model governance and guardrails
- Translate AI outputs into actionable insights
Perks/Benefits
- N/A
Skills/Tech-stack
CI/CD | Continuous Training | Data Drift | Data Drift Detection | Deep learning | Drift Detection | Knowledge Distillation | MLOps | Machine Learning | Model Governance | Model Monitoring | Model retraining | Pandas | Pruning | PyTorch | Python | Quantization | Reinforcement Learning | Supervised Learning | TensorFlow | Unsupervised Learning | Version control
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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