Key Responsibilities
- Design, develop, train, and evaluate machine learning models.
- Develop AI/ML solutions for business and technical problems.
- Perform data preprocessing, feature engineering, and exploratory data analysis.
- Implement supervised and unsupervised machine learning algorithms.
- Develop and optimize deep learning models using frameworks such as TensorFlow or PyTorch.
- Build and maintain end-to-end machine learning pipelines.
- Deploy ML models into production using cloud and containerization technologies.
- Monitor model performance and improve accuracy, scalability, and reliability.
- Work with data engineers, software developers, and business teams to integrate ML solutions.
- Conduct model validation, testing, and performance tuning.
- Stay updated with emerging technologies in AI, machine learning, Generative AI, and Large Language Models (LLMs).
- Document models, experiments, technical processes, and deployment workflows.
Required Skills
- Strong proficiency in Python.
- Good knowledge of Machine Learning and Deep Learning.
- Experience with scikit-learn, TensorFlow, PyTorch, or similar frameworks.
- Strong understanding of statistics, probability, and data analysis.
- Experience with SQL and databases.
- Knowledge of data preprocessing and feature engineering.
- Familiarity with Git, Docker, APIs, and CI/CD.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP).
- Understanding of ML model deployment and MLOps practices.