Generative AI Engineer

Exp- 3yr+

AI Model Development: Design and develop machine learning and deep learning models to solve business problems, leveraging techniques such as classification, regression, clustering, and natural language processing (NLP).
Data Collection and Preprocessing: Gather, clean, and preprocess large volumes of structured and unstructured data for model training and evaluation. Perform exploratory data analysis to gain insights and identify patterns.
Model Training and Evaluation: Train and fine-tune machine learning models using state-of-the-art algorithms and frameworks. Evaluate model performance using appropriate metrics and techniques and iterate on model designs to improve accuracy and efficiency.
Model Deployment: Deploy AI models into production environments, ensuring scalability, reliability, and performance. Collaborate with DevOps and IT teams to integrate models into existing systems and infrastructure.
Algorithm Optimization: Optimize algorithms and model architectures for performance, speed, and resource efficiency. Implement parallel processing, distributed computing, and other optimization techniques to enhance model scalability and throughput.
Collaboration and Communication: Collaborate effectively with cross-functional teams, including data scientists, software engineers, and business stakeholders. Communicate technical concepts and findings to non-technical audiences in a clear and understandable manner.
Research and Innovation: Stay abreast of the latest developments in artificial intelligence, machine learning, and related fields. Conduct research and experimentation to explore new techniques and approaches and contribute to the organization's innovation initiatives.
Documentation and Best Practices: Document code, models, and processes to ensure reproducibility and maintainability. Follow best practices for software development, version control, and project management.
Proficiency in programming languages such as Python, Java, or C++.
Strong understanding of machine learning algorithms and frameworks, such as TensorFlow, PyTorch, or scikit-learn.
Experience with data preprocessing, feature engineering, and model evaluation techniques.
Knowledge of cloud computing platforms (e.g., AWS, Azure, Google Cloud) and containerization technologies (e.g., Docker, Kubernetes) is a plus.