AI / Machine Learning Engineer
Tasks
- Address model drift
- Build model serving infrastructure
- Deploy models to production
- Develop data pipelines
- Develop machine learning models
- Document methodologies and processes
- Ensure data quality
- Ensure governance and regulatory compliance
- Evaluate machine learning frameworks
- Evaluate model performance
- Implement testing and validation
- Integrate with enterprise systems
- Monitor model performance
- Optimize data collection transformation and storage
- Perform feature engineering
- Perform model lifecycle management
- Prepare datasets
- Process large datasets
- Recommend AI architecture enhancements
- Research innovative approaches
- Retrain machine learning models
- Stay current with AI advancements
- Train deep learning models
- Train supervised models
- Train unsupervised models
Perks/Benefits
Skills/Tech-stack
CI/CD | Cloud Computing | Computer Vision | Data Pipelines | Data Preprocessing | Data Quality | Deep learning | Distributed Computing | Docker | ETL | Feature Engineering | Generative AI | Kubernetes | Language Models | Language Processing | Large Language Models | MLOps | Machine Learning | Model Optimization | Model Serving | Natural Language | Natural Language Processing | Predictive Analytics | PyTorch | Python | Recommendation Systems | Scikit-learn | Statistical modeling | Supervised Learning | TensorFlow | Unsupervised Learning | Version control | XGBoost
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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