Ai/ml Intern

7 months ago

Bengaluru Karnataka, India Dotcod Innovation Private Limited Full time

Key Responsibilities:

- **Collaborate with the AI/ML team**: Work closely with experienced data scientists and engineers to develop and implement machine learning models and algorithms.
- **Python Programming**: Write clean, efficient, and well-documented Python code for various AI/ML projects.
- **Data Structures**: Utilize and optimize data structures for efficient data manipulation and processing.
- **Deep Learning**: Design, train, and evaluate deep learning models for tasks such as image recognition, speech processing, and predictive analytics.
- **Natural Language Processing (NLP)**: Develop and implement NLP models and algorithms for tasks such as text classification, sentiment analysis, and language generation.
- **Data Preprocessing**: Clean, preprocess, and analyze large datasets to prepare them for model training and evaluation.
- **Model Evaluation**: Assist in evaluating the performance of machine learning models using appropriate metrics and techniques.
- **Documentation**: Maintain comprehensive documentation of models, algorithms, and code to ensure reproducibility and knowledge sharing within the team.

Required Skills and Qualifications:

- **Python**: Proficiency in Python programming, with a solid understanding of libraries such as NumPy, pandas, and scikit-learn.
- **Data Structures**: Strong knowledge of data structures (e.g., arrays, linked lists, trees, graphs) and algorithms.
- **Deep Learning**: Experience with deep learning frameworks such as TensorFlow, Keras, or PyTorch.
- **NLP**: Familiarity with NLP techniques and libraries such as NLTK, spaCy, or Hugging Face Transformers.
- **Analytical Skills**: Strong problem-solving skills and the ability to work with complex datasets.
- **Communication**: Good written and verbal communication skills, with the ability to explain technical concepts to non-technical stakeholders.
- **Team Player**: Ability to work effectively both independently and as part of a team.

Schedule:

- Day shift

Work Location: In person

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