Machine Learning Engineer - GCH
Tasks
- Apply MLOps for model versioning retraining monitoring and scalability
- Apply predictive analytics for business strategy
- Build, train, and deploy machine learning models
- Collaborate with data scientists data engineers and software teams
- Conduct ongoing model evaluation and improvement
- Deploy models on AWS GCP or Azure
- Design and optimize ETL ELT pipelines for ML data
- Design end-to-end ML/AI pipelines
- Develop conversational AI and recommendation systems
- Document code, models, and processes
- Engineer and preprocess large complex datasets
- Evaluate model interpretability explainability and bias
- Implement NLP deep learning and computer vision solutions
- Optimize models for latency, accuracy, and scalability
- Partner with stakeholders to identify ML AI opportunities
- Perform continuous integration and deployment for models
- Research and apply new ML AI methodologies and LLMs
Perks/Benefits
- N/A
Skills/Tech-stack
AI Platform | AWS SageMaker | Apache Airflow | Artificial Intelligence | Azure Machine Learning | Bias reduction | Classification | Cloud AI Platform | Cloud Computing | Clustering | Computer Vision | Deep learning | Docker | ELT | ETL | Google Cloud | Google Cloud AI | Google Cloud AI Platform | Hugging Face | Keras | Kubernetes | Language Models | Language Processing | Large Language Models | MLOps | MLflow | Machine Learning | Model Interpretability | Natural Language | Natural Language Processing | Predictive Analytics | PyTorch | Python | Recommendation Systems | Regression | Scikit-learn | TensorFlow
Education
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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