Responsibilities of the Intern:

Work with Product Managers, senior engineers and peers to understand requirements, prototype & build large scale distributed applications
Develop and implement custom machine learning models
Clean, preprocess, and analyze large datasets, ensuring data quality and integrity.
Address and manage imbalanced datasets to optimize model performance.
Deploy and host machine learning models, ensuring their scalability and reliability in production environments.
Monitor and maintain models in production, implementing feedback loops for continuous improvement.
Identify and mitigate biases in models to ensure fairness and accuracy.
Collaborate with cross-functional teams to understand business needs and translate them into technical requirements.
Document processes and model performance, providing insights and recommendations.

Requirements:

Bachelor's/master's degree in computer/software engineering, Computer Science or related concentration, equivalent and/or combination of education.
A solid foundation in Computer Science fundamentals, algorithms, design patterns, CI/CD processes, and data structures.
Proficiency in Python or R, with experience in libraries such as TensorFlow, PyTorch, or Scikit-learn.
Strong understanding of data preprocessing techniques and handling imbalanced datasets.
Familiarity with deploying models using cloud platforms (AWS, GCP, or Azure).
Knowledge of model evaluation metrics and techniques to identify and reduce bias.
Excellent problem-solving skills and attention to detail.
Strong communication skills, with the ability to explain complex concepts to non-technical stakeholders.
Good understanding of LLM architecture, fine-tuning, and training LLMs.
Robust understanding of AI agents.
Superb ability to take high level direction, work through ambiguity and drive to execution in an iterative model.
Superb ability to translate business requirements into technical design & architecture.
Ability to communicate ideas clearly and effectively.