• Deploys machine learning models into production environments.

• Integrates models seamlessly with existing applications and infrastructure.

• Manages the cloud and on-prem infrastructure.

• Ensures reliability, scalability, and cost efficiency across environments.

• Ensures ethical standards and regulatory compliance in AI systems.

• Identifies and mitigates bias, fairness, and transparency concerns.

• Secures AI models and data pipelines from potential threats.

• Applies best practices for encryption, access control, and secure deployment.

• Builds and maintains robust data pipelines for training and inference.

• Collaborates with data scientists to ensure scalable, high-quality data workflows.

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• Coordinates high-quality data annotation tasks for model training.

• Maintains accuracy and consistency across labeled datasets.

• Provides technical assistance to end-users of AI tools.

• Troubleshoots issues and offers guidance on product usage.

• Implements CI/CD pipelines for automated testing and model deployment.

• Enables a faster, more reliable ML development lifecycle.

• Assists in developing and optimizing machine learning algorithms.

• Troubleshoots and fine-tunes models to improve accuracy and efficiency.

• Continuously monitors deployed models for performance and drift.

• Implements updates and retraining strategies to maintain accuracy with documents.

• Develops user training programs and internal onboarding content.

• Documents models, APIs, and end-to-end ML workflows with quality checks.

• Supports innovation through cutting-edge ML and GenAI research.

• Explores new algorithms and stays current with the latest advancements.