Overview

We are seeking a talented AI/ML Developer to join our team and build cutting-edge Generative AI models.

Responsibilities

• We are seeking a talented AI/ML Developer to join our team and build cutting-edge Generative AI models.
• Develop and fine-tune generative models using techniques like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformers, Diffusion models, etc.
• Work on tasks such as text generation, image generation, music or sound generation, video creation, and other creative AI applications.
• Design, build, and deploy models using frameworks like TensorFlow, PyTorch, Hugging Face, OpenAI's GPT, or similar tools.
• Optimize AI models for performance, accuracy, and scalability in real-world production environments.
• Research and stay updated on the latest trends, papers, and breakthroughs in generative AI.
• Collaborate with other teams (data scientists, machine learning engineers, product teams) to integrate models into production systems.
• Work on data preprocessing, augmentation, and designing pipelines to ensure high-quality input for model training.
• Document your code, process, and model outputs for team-wide visibility and knowledge sharing.

Qualifications

• The candidate requires to carry expertise in machine learning, deep learning, and natural language processing (NLP), as well as an
• understanding of specific generative models like GPT, GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), or other types of neural networks.
• Bachelor's or Master's degree in Computer Science or related fields.
• 3-4 years of hands-on experience in developing Generative AI solutions
• Strong understanding of deep learning algorithms, especially in generative models like GANs, VAEs, Diffusion models, or large-scale language models like GPT.
• Hands-on experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, or Hugging Face.
• Experience working with neural networks, unsupervised learning, and natural language processing (NLP).
• Strong programming skills in Python, including deep learning libraries (e.g., TensorFlow, Keras, PyTorch).
• Familiarity with cloud platforms (AWS, GCP, or Azure) for model training and deployment.
• Strong mathematical and statistical knowledge, particularly in probability theory, linear algebra, and optimization.
• Experience in large-scale model training, fine-tuning, or distributed computing.
• Knowledge of reinforcement learning (RL) and self-supervised learning.
• Familiarity with AI ethics, bias mitigation, and interpretability in generative models .