- Research and Development:
- Researching and exploring machine learning algorithms, techniques, and frameworks.
- Experimenting with different models to solve specific problems like classification, regression, clustering, etc.
- Developing and fine-tuning algorithms for accuracy, scalability, and efficiency.
- Data Handling and Preparation:
- Collecting, cleaning, and preprocessing data for model training and evaluation.
- Performing exploratory data analysis to understand patterns and insights within the data.
- Implementing data pipelines to automate data extraction, transformation, and loading (ETL) processes.
- Model Building and Training:
- Designing and building machine learning models using tools and libraries such as TensorFlow, PyTorch, Scikit-learn, etc.
- Training models with large datasets and optimizing hyperparameters for better performance.
- Validating and evaluating model performance using appropriate metrics and techniques.
- Deployment and Integration:
- Deploying machine learning models into production environments, often using cloud services or containerization (e.g., Docker, Kubernetes).
- Integrating ML models with existing systems or applications to automate decision-making processes.
- Monitoring model performance post-deployment and implementing necessary updates or improvements.
- Collaboration and Communication:
- Collaborating with cross-functional teams including data scientists, software engineers, and domain experts.
- Communicating complex technical concepts and findings to non-technical stakeholders.
- Contributing to team knowledge sharing and mentoring junior team members.
- Ethical and Legal Considerations:
- Understanding ethical implications of AI applications, such as bias and fairness in algorithms.
- Adhering to legal and regulatory requirements related to data privacy and security (e.g., GDPR, HIPAA).
Job Category: Development
Job Type: Full Time
Job Location: Hyderabad