Key Responsibilities :

- Design, develop, and implement machine learning models for classification, regression, NLP, recommendation systems, or computer vision tasks.

- Perform data preprocessing, feature engineering, and exploratory data analysis (EDA) to prepare datasets for modeling.

- Develop ML pipelines and integrate models into production environments using APIs or cloud services.

- Optimize and fine-tune models for accuracy, efficiency, and scalability.

- Collaborate with data engineers, software developers, and business stakeholders to define requirements and deliver AI-driven solutions.

- Monitor model performance and retrain/update models as necessary.

- Document workflows, models, and processes to ensure reproducibility and knowledge sharing.

Required Technical Skills :

- Programming Languages : Python (primary), R or Java (optional).

- ML Frameworks : TensorFlow, PyTorch, scikit-learn, Keras.

- Data Manipulation & Analysis : Pandas, NumPy, Matplotlib, Seaborn.

- Databases : SQL, NoSQL (MongoDB, Cassandra, or equivalent).

- Deployment & Cloud : AWS SageMaker, Azure ML, or GCP AI Platform.

- Algorithms : Supervised and unsupervised learning, NLP, deep learning, and reinforcement learning basics.

- Tools : Git, Jupyter Notebooks, Docker, and CI/CD pipelines for ML