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:

• Bachelor’s degree in Computer Science, Data Science, AI, or a related field
• Master's/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)

• 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

• MLOps practices
• CI/CD for ML
• Containerization (Docker, Kubernetes)
• ML frameworks (PyTorch, TensorFlow)
• Cloud platforms (AWS, Azure, GCP)

• Bachelor’s degree in Computer Science, Data Science, AI, or a related field
• Master's/PhD preferred in Machine Learning or a related discipline