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Location: Atlanta, GA, NY, NJ, Phoenix, AZ, Irving, TX (Preferred) - Onsite Role
We are looking for hands-on experience with Production ML model deployment using open ML frameworks like Pytorch, Tensorflow, Xgboost in Kubernetes cluster.
Job Responsibilities
In this role, you will not use low-code or no-code ML platforms. Instead, you will build and deploy ML modules in a production environment using Kubernetes clusters on public cloud. The ideal candidate will have a strong background in Python, a deep understanding of Machine Learning algorithms and models, and the ability to scale them and their training processes extensively within one of the largest companies in the United States. We are seeking professionals experienced in deploying solutions from scratch into production environments using Kubernetes clusters on public cloud platforms.
Required Experience
- Design, implement, and optimize machine learning algorithms and models, including Neural networks (Graph neural network), Deep learning & transformers (Temporal Fusion Transformer), NLP, gradient boosting using open source machine learning frameworks like TensorFlow, PyTorch, XGBoost, and LightGBM.
- Enable functionality to support analysis, model optimization, statistical testing, model versioning, deployment, and monitoring of model and data.
- Ability to translate functionality into scalable, tested, and configurable platform architecture and software.
- Establish strong software engineering principles for development in Python on the Azure Kubernetes Platform.
- Strong ownership of deliverables, with design decisions aligned to scale and industry best practices.
- Collaborate with cross-functional teams to align ML initiatives with overall business goals.
- Implement scalable and efficient machine learning systems. Collaborate with software engineers to integrate ML models into production systems.
- Work closely with data engineers to ensure the availability and quality of data for training and evaluation of machine learning models.
- Develop strategies for deploying machine learning models at scale. Ensure models are integrated into production systems with high reliability and performance.
- Design and conduct experiments to evaluate the performance of machine learning models. Iterate on models based on feedback and evolving business requirements.
Required Education
- Bachelor's degree in computer science or another equivalent degree.
Position: Sr Machine Learning Engineer
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