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info@flexsin.comGenerative AI Engineer - Generative AI
Bringing the brightest minds to collaborate and drive mutual success.Job Description:
We are seeking a talented Generative AI Developer to join our team and build cutting-edge generative models. The ideal candidate will work on developing AI systems that can create innovative, human-like content, ranging from natural language generation (NLG), image generation, and even video or music synthesis.
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.
Responsibilities:
- 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.
REQUIRED QUALIFICATIONS & SKILLS:
- 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.