Machine Learning Intern

3 days ago

Mansarovar Jaipur Rajasthan, India Geek Shastra Technologies Full time

We are seeking a talented Machine Learning Developer to join our team. In this role, you will be responsible for designing, implementing, and optimizing machine learning models and algorithms to solve complex problems and drive innovation. You will work closely with data scientists, engineers, and stakeholders to deliver impactful solutions.

**Key Responsibilities**:

- Develop and implement machine learning models and algorithms to solve specific problems.
- Design and conduct experiments to validate and optimize models.
- Collaborate with cross-functional teams to integrate machine learning solutions into production systems.
- Analyze and preprocess large datasets to extract meaningful insights and features.
- Evaluate model performance and iterate to improve accuracy and efficiency.
- Stay updated with the latest research and trends in machine learning and artificial intelligence.
- Document processes, code, and results to ensure clarity and reproducibility.

**Required Skills**:

- Proficiency in programming languages such as Python, R, or Java.
- Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- Strong understanding of machine learning algorithms and statistical methods.
- Experience with data preprocessing and feature engineering.
- Knowledge of version control systems (e.g., Git).
- Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) is a plus.
- Ability to work with large datasets and perform data wrangling.
- Strong problem-solving skills and attention to detail.

**Preferred Qualifications**:

- Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related field.
- Experience with deep learning and neural networks.
- Knowledge of software engineering best practices and design patterns.
- Experience with deployment tools and methodologies (e.g., Docker, Kubernetes)

**Experience**:

- total work: 1 year (preferred)

Work Location: In person

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