ML and Analytics Programs

Model Deployment Engineer:

• Responsible for deploying machine learning models into production environments.

• Ensures smooth integration of models with existing systems and applications.

Data Engineer:

• Manages data pipelines and ensures data quality for training and inference.

• Collaborates with data scientists to design efficient data processing workflows.

Algorithm Support Specialist:

• Assists in the development and optimization of machine learning algorithms.

• Provides expertise in algorithmic techniques and troubleshooting during model training.

AI Infrastructure Support:

• Manages and maintains the infrastructure supporting AI workloads.

• Optimizes cloud resources and ensures scalability of AI systems.

Data Annotation and Labeling Support:

• Coordinates data annotation tasks for training datasets.

• Ensures the quality and accuracy of labeled data for model training.

Model Monitoring and Maintenance:

• Monitors deployed models for performance and accuracy.

• Addresses issues such as model drift and degradation, and implements necessary updates.

AI Ethics and Compliance Officer:

• Ensures that AI models comply with ethical standards and regulations.

• Addresses bias and fairness concerns in AI systems.

Customer Support for AI Products:

• Provides technical support for users of AI products.

• Assists with troubleshooting and guides users on how to maximize the value of AI solutions.

Training and Documentation Specialist:

• Creates training materials for internal teams and external users.

• Develops documentation for models, APIs, and data processing workflows.

AI Security Specialist:

• Focuses on securing AI systems from potential attacks and vulnerabilities.

• Implements security best practices for data storage, model deployment, and communication.

Continuous Integration/Continuous Deployment (CI/CD) Engineer:

• Sets up and maintains CI/CD pipelines for automated testing and deployment of AI models.

• Ensures a streamlined and efficient development lifecycle.

Research and Development (R&D) Support:

• Supports research initiatives, explores new algorithms, and stays updated on the latest advancements in ML and GenAI.