Lead AI/ML Engineer
Tasks
- Benchmark model performance
- Build data pipelines
- Collaborate with QA for model validation monitoring reliability
- Create scalable AI system architectures
- Create standardized ML workflows
- Deploy machine learning models
- Deploy models using MLOps practices
- Design machine learning models
- Develop deep learning models
- Develop reusable AI components
- Ensure model scalability performance security
- Evaluate machine learning models
- Implement NLP solutions
- Implement computer vision solutions
- Implement model monitoring drift bias detection
- Implement predictive analytics
- Implement recommendation systems
- Integrate AI features with software engineers
- Maintain AI documentation and knowledge repositories
- Manage data with DBA collaboration
- Mentor junior AI ML engineers and data scientists
- Optimize algorithms speed accuracy resource utilization
- Optimize training validation inference pipelines
- Review model code and experimentation frameworks
- Track model performance
- Tune machine learning models
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Agile | Amazon SageMaker | Azure | Cloud platform | Computer Vision | Data Pipelines | Docker | Feature Engineering | Generative AI | Google Cloud | Google Cloud Platform | Kubeflow | Kubernetes | LLMs | MLOps | MLflow | Model Optimization | NLP | Predictive Analytics | PyTorch | Python | REST APIs | Recommendation Systems | SDLC | Scikit-learn | TensorFlow
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI Engineer | Engineer | Lead | Lead AI | Lead AI Engineer | Learning Engineer | Machine Learning Engineer
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