Design, develop, and implement generative AI models (e.g., GANs, VAEs, Transformers) for various applications such as text, image, audio, or video generation.
Build scalable Python pipelines for training, fine-tuning, and deploying large language models and other generative architectures.
Collaborate with data scientists, ML engineers, and product teams to integrate AI models into products and platforms.
Optimize model performance, including speed, accuracy, and resource consumption.
Conduct experiments to improve model robustness and creativity.
Stay up-to-date with the latest research in generative AI and incorporate new techniques into our stack.
Write clean, maintainable code and maintain proper documentation.
Mentor junior engineers and contribute to code reviews.
Mandatory skill sets
Strong proficiency in Python programming and related AI/ML libraries (TensorFlow, PyTorch, Hugging Face Transformers, etc.).
Solid understanding of generative models such as GANs, VAEs, autoregressive models, and diffusion models.
Experience with training and fine-tuning large language models.
Familiarity with cloud platforms (AWS, GCP, Azure) and containerization tools like Docker/Kubernetes.
Knowledge of data preprocessing, augmentation, and handling large datasets.
Experience implementing end-to-end AI model deployment pipelines.
Ability to write efficient, optimized code and profile AI workloads.
Preferred skill sets
Experience with prompt engineering and working with large-scale LLMs (e.g., GPT, T5, BERT).
Knowledge of reinforcement learning or unsupervised learning techniques.
Familiarity with MLOps and CI/CD tools for model deployment.
