1. Algorithm Development and NLP:

o Design, implement, and optimize machine learning algorithms and models.

o Utilize NLP techniques for tasks such as sentiment analysis, named entity recognition, and language translation.

o Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.

2. Data Engineering & Integrations:

o Perform data preprocessing and feature engineering to ensure high- quality input for machine learning models.

o Work with large datasets, ensuring scalability and efficiency in data processing pipelines.

o Collaborate with data engineers to integrate machine learning solutions into existing data infrastructure.

3. Model Training and Evaluation:

o Train, fine-tune, and evaluate machine learning models using state-of-the-art techniques.

o Implement and improve model validation and testing strategies.

4. Deployment and Integration:

o Build end to end scalable pipeline for executing the models along with integrations with other systems and applications.

o Deploy machine learning models into production environments.

o Collaborate with software engineers to integrate models into existing systems and applications.

5. Continuous Learning:

o Stay abreast of the latest developments in machine learning and related fields.

o Continuously enhance your skills and knowledge through training and self-directed learning.