Key Responsibilities
* Developing AI / Gen AI Models and Algorithms:

   * Designing, building, and training machine learning and deep learning models.

   * Implementing algorithms for tasks like natural language processing (NLP), computer vision, and predictive analytics.

* Integrating AI into Applications:

   * Embedding AI functionalities into existing software systems and applications.

   * Developing APIs and interfaces for AI-powered features.

* Data Handling and Processing:

   * Working with large datasets, including data cleaning, preprocessing, and feature engineering.

   * Ensuring data quality and integrity for AI model training.

* Collaboration and Communication:

   * Working closely with data scientists, engineers, and stakeholders.

   * Communicating technical concepts to non-technical audiences.

* Optimization and Deployment:

   * Optimizing AI models for performance, scalability, and efficiency.

   * Deploying AI solutions to production environments.

* Staying Current with AI Trends:

   * Continuously learning and researching the latest AI technologies and advancements.

Work Experience

* Programming Languages:

   * Proficiency in Python, and potentially other languages like Java, or C++.

* Machine Learning and Deep Learning Frameworks:

   * Experience with libraries like TensorFlow, PyTorch, and scikit-learn.

 * Gen AI  

   Experience of various generative models like GPT, VAE, GANs, and Diffusion   Models is crucial. 

* Data Science Skills:

   * Knowledge of data analysis, statistical modeling, and data visualization.

* Cloud Computing:

   * Familiarity with cloud platforms like AWS, Google Cloud, or Azure.

* Software Development Skills:

   * Understanding of software development principles and practices.