Generative AI Engineer

• Design, develop, and train generative models (such as GANs, VAEs, Diffusion models, Transformer-based models like GPT, etc.) for various use cases, including image, text, and speech generation.
• Fine-tune large pre-trained models to specific domains and tasks.
• Research and implement state-of-the-art techniques for improving model accuracy, efficiency, and scalability.
• Work with large-scale datasets, including data cleaning, annotation, augmentation, and preparation for model training.
• Collaborate with Data Engineering teams to ensure proper data pipelines and infrastructure are in place.
• Develop and deploy generative AI models into production systems.
• Optimize models for inference speed, accuracy, and efficiency in cloud or edge environments.
• Ensure models are scalable, reliable, and maintainable.
• Stay up-to-date with the latest advancements in generative AI and related fields.
• Participate in AI research projects, publish papers, or contribute to open-source communities.
• Innovate and prototype new applications for generative AI.
• Work closely with software engineers, product managers, and domain experts to integrate AI solutions into existing systems and workflows.
• Translate technical concepts to non-technical stakeholders.
• What are we looking for
• Bachelor s or Master s degree in Computer Science, AI, Machine Learning, or a related field (PhD preferred).
• 3+ years of experience working with deep learning frameworks (e.g., TensorFlow, PyTorch).
• Strong background in machine learning algorithms, specifically generative models like GANs, VAEs, transformers, etc.
• Proficiency in Python and experience with other programming languages like Java or C++ is a plus.
• Experience with cloud platforms (AWS, GCP, or Azure) and deploying AI models in production.
• Solid understanding of deep learning techniques, neural networks, and generative adversarial models.
• Strong analytical, problem-solving, and debugging skills.

Preferred Qualifications

• Experience with NLP, computer vision, and multimodal models.
• Familiarity with reinforcement learning techniques in generative modeling.
• Experience working with tools like Hugging Face, OpenAI API, and similar AI/ML libraries.
• Knowled publication record in top-tier AI/ML conferences or journals is an advantage.ge of MLOps tools and practices for continuous deployment and monitoring of AI models.
• A strong