Roles and Responsibilities:
- AI Solution Design: Architect, design, and develop end-to-end AI and machine learning solutions addressing complex business problems across multiple domains.
- Model Development & Optimization: Build, train, and optimize machine learning and deep learning models using frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Data Pipeline Management: Work closely with data engineering teams to design efficient data pipelines, ensuring data quality, scalability, and real-time availability.
- Integration & Deployment: Implement and deploy AI models into production using MLOps tools and platforms such as Docker, Kubernetes, MLflow, or AWS Sagemaker.
- Research & Innovation: Stay updated with emerging trends in AI, NLP, computer vision, and generative AI to propose and prototype new solutions.
- Performance Evaluation: Evaluate model performance using relevant metrics and continuously improve models through retraining, hyperparameter tuning, and feedback integration.
- Collaboration: Partner with cross-functional teams including data scientists, software engineers, and product managers to translate AI models into business value.
- Mentorship: Guide junior engineers and data scientists, providing technical direction and ensuring adherence to best practices in AI development.
