Experience: 2+ years
Job description
We are looking for a Generative AI Engineer to join our innovative team and help drive cutting-edge solutions using advanced AI techniques. The ideal candidate will have strong expertise in machine learning, particularly in generative models, and be passionate about pushing the boundaries of artificial intelligence. You will work on projects that leverage models like GPT, GANs, VAEs, and others to create groundbreaking AI-generated content, automate processes, and solve real-world problems.
Key Responsibilities:
- Design, implement, and optimize Generative AI models (e.g., GPT, GANs, VAEs) for various applications such as text, image, video, and audio generation.
- Develop and deploy scalable AI solutions that leverage generative models for content creation, data augmentation, and predictive analysis.
- Work with deep learning frameworks such as TensorFlow, PyTorch, or JAX to build, train, and fine-tune models.
- Collaborate with cross-functional teams (data scientists, product managers, and engineers) to integrate AI models into production environments.
- Research and experiment with state-of-the-art generative AI techniques, staying updated on the latest advancements in the field.
- Analyze and evaluate model performance, identifying bottlenecks and optimizing model architecture and training strategies.
- Build and maintain datasets required for training generative models, ensuring data integrity and quality.
- Implement best practices for model interpretability, debugging, and troubleshooting issues with generative outputs.
- Develop APIs and deploy models to cloud platforms (e.g., AWS, GCP, Azure) for scalable AI services.
- Ensure ethical considerations in AI model development, addressing biases and ensuring model transparency.
Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.
- Proven experience in generative models such as GPT (transformers), GANs (Generative Adversarial Networks), or VAEs (Variational Autoencoders).
- Proficiency in machine learning frameworks such as TensorFlow, PyTorch, or JAX.
- Strong programming skills in Python and experience with libraries like Hugging Face Transformers, Keras, and OpenAI API.
- Experience with NLP (Natural Language Processing), computer vision, or audio processing using AI models.
- Familiarity with cloud computing platforms (AWS, GCP, Azure) for deploying models at scale.
- Strong understanding of deep learning architectures (RNNs, CNNs, transformers, etc.) and optimization techniques.
- Experience with Docker, Kubernetes, and other tools for containerization and orchestration is a plus.
- Ability to work with large datasets and big data technologies (e.g., Hadoop, Spark) is an advantage.
