About this Position:
Our client is a technology driven organization delivering high impact AI and machine learning solutions for enterprise and government clients. This role is critical in designing building and deploying scalable AI/ML systems while contributing to proprietary intellectual property. The ideal candidate is a highly skilled Machine Learning Engineers with strong production experience capable of working on complex large-scale projects in secure and regulated environments.
Key Responsibilities:
- Design and build machine learning models and pipelines.
- Implement MLOps pipelines using tools like Kubeflow MLflow or Airflow.
- Scale AI infrastructure and integrate models with hybrid environments (on-prem GPUs cloud providers like AWS Azure etc.)
- Lead and contribute to client-facing AI/ML solutions for enterprise and government projects.
- Ensure security compliance and scalability for multi-million-dollar contracts
- Contribute to the development of proprietary AI/ML intellectual property.
- Drive innovation and stay ahead of emerging AI trends
Requirements
Qualifications:
- Bachelors degree in Computer Science Data Science AI or a related field
- Masters/PhD preferred in Machine Learning or a related discipline
- 5 years in machine learning engineering with hands-on experience building production ML systems
- Expertise in ML frameworks (PyTorch TensorFlow) and cloud platforms (AWS Azure GCP)
- Experience in MLOps practices CI/CD for ML and containerization (Docker Kubernetes)
HOW TO APPLY
Qualified candidates are encouraged to share their CV with us via:
Email Subject Line: Machine Learning Engineer
Applications will be reviewed on a rolling basis.
Required Skills:
5 years in machine learning engineering with hands-on experience building production ML systems Expertise in ML frameworks (PyTorch TensorFlow) and cloud platforms (AWS Azure GCP) Experience in MLOps practices CI/CD for ML and containerization (Docker Kubernetes)
Required Education:
Bachelors degree in Computer Science Data Science AI or a related fieldMasters/PhD preferred in Machine Learning or a related discipline
About this Position:Our client is a technology driven organization delivering high impact AI and machine learning solutions for enterprise and government clients. This role is critical in designing building and deploying scalable AI/ML systems while contributing to proprietary intellectual property....
About this Position:
Our client is a technology driven organization delivering high impact AI and machine learning solutions for enterprise and government clients. This role is critical in designing building and deploying scalable AI/ML systems while contributing to proprietary intellectual property. The ideal candidate is a highly skilled Machine Learning Engineers with strong production experience capable of working on complex large-scale projects in secure and regulated environments.
Key Responsibilities:
- Design and build machine learning models and pipelines.
- Implement MLOps pipelines using tools like Kubeflow MLflow or Airflow.
- Scale AI infrastructure and integrate models with hybrid environments (on-prem GPUs cloud providers like AWS Azure etc.)
- Lead and contribute to client-facing AI/ML solutions for enterprise and government projects.
- Ensure security compliance and scalability for multi-million-dollar contracts
- Contribute to the development of proprietary AI/ML intellectual property.
- Drive innovation and stay ahead of emerging AI trends
Requirements
Qualifications:
- Bachelors degree in Computer Science Data Science AI or a related field
- Masters/PhD preferred in Machine Learning or a related discipline
- 5 years in machine learning engineering with hands-on experience building production ML systems
- Expertise in ML frameworks (PyTorch TensorFlow) and cloud platforms (AWS Azure GCP)
- Experience in MLOps practices CI/CD for ML and containerization (Docker Kubernetes)
HOW TO APPLY
Qualified candidates are encouraged to share their CV with us via:
Email Subject Line: Machine Learning Engineer
Applications will be reviewed on a rolling basis.
Required Skills:
5 years in machine learning engineering with hands-on experience building production ML systems Expertise in ML frameworks (PyTorch TensorFlow) and cloud platforms (AWS Azure GCP) Experience in MLOps practices CI/CD for ML and containerization (Docker Kubernetes)
Required Education:
Bachelors degree in Computer Science Data Science AI or a related fieldMasters/PhD preferred in Machine Learning or a related discipline
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