Model Development: Design, implement, and optimize generative models (e.g., GANs, VAEs, Transformers) for various applications, including text, image, and audio generation.
Data Management: Work with large datasets, perform data preprocessing, augmentation, and ensure the quality and integrity of training data.
Research and Innovation: Stay up to date with the latest research in generative AI and machine learning and apply new techniques to enhance model performance and capabilities.
Collaboration: Collaborate with cross-functional teams to understand project requirements, define AI solutions, and integrate models into production systems.
Performance Optimization: Monitor and optimize the performance of generative models, including tuning hyperparameters, reducing latency, and improving the scalability of AI systems.
Documentation: Maintain comprehensive documentation of model architectures, training processes, and performance metrics.
Testing and Validation: Develop and implement rigorous testing and validation protocols to ensure the robustness and reliability of AI models.
Mentorship: Provide guidance and mentorship to junior developers and interns, fostering a collaborative and growth-oriented work environment.
Soft Skills:
Excellent problem-solving and analytical skills.
Strong communication skills and ability to work effectively in a team environment.
Ability to manage multiple projects and meet deadlines.
Required qualifications:
Bachelor's or master’s degree in computer science, Engineering, or a related field.
3+ years of experience in AI/ML development life cycle, with a focus on generative models.
Proficiency in Python and relevant ML libraries/frameworks (e.g., StreamLit, TensorFlow, PyTorch, Scikit-Learn).
Experience with generative models such as GANs, VAEs, and Transformer-based architectures.
Strong understanding of deep learning techniques, neural network architectures and software engineering principles.
Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and containerization (e.g., Docker, Kubernetes).
Experience with data preprocessing, augmentation, and working with large-scale datasets.
Preferred qualifications:
Experience with NLP, computer vision, or audio processing.
Knowledge of reinforcement learning or unsupervised learning techniques.
Contributions to open-source projects in the AI/ML domain.