Generative AI Engineer Opportunity in Chennai with Cognizant

Cognizant is seeking a talented Generative AI Engineer to join their team. This role involves working on cutting-edge AI technologies to develop and deploy innovative solutions. The interview process will be conducted in person.

Responsibilities:

• Prepare and clean multimodal foundation models for training and evaluation, including generating synthetic data.
• Develop and optimize large-scale generative AI models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
• Engage in tasks related to language modeling, text generation, comprehension, and contextual understanding.
• Regularly review and fine-tune Large Language Models (LLMs) to ensure accuracy and relevance for custom datasets.
• Build and deploy AI applications on major cloud platforms (Azure, GCP, AWS).
• Integrate AI models with company data to enhance and augment existing applications.
• Handle data preprocessing, augmentation, and synthetic data generation.
• Design and develop backend services using Python or .NET to support LLM-powered solutions.
• Develop and maintain AI pipelines.
• Work with custom datasets, employing techniques like chunking and embeddings for model training and fine-tuning.
• Integrate Azure Cognitive Services or equivalent platform services to enhance AI solutions.
• Collaborate with cross-functional teams for seamless deployment and integration of AI solutions.
• Ensure the robustness, efficiency, and scalability of AI systems.
• Stay abreast of the latest advancements in AI and machine learning.

Skills and Experience Required:

• Strong foundation in machine learning, deep learning, and computer science.
• Expertise in generative AI models and techniques (e.g., GANs, VAEs, Transformers).
• Experience with natural language processing (NLP) and computer vision is advantageous.
• Ability to work effectively both independently and as part of a team.
• Proficiency in advanced programming, particularly Python, and AI-centric libraries like TensorFlow, PyTorch, and Keras.
• Knowledge of NLP for text generation, text parsing, sentiment analysis, and transformer models like GPT.
• Experience in data management, including preprocessing, augmentation, and synthetic data generation.
• Proven experience in developing and deploying AI models in production environments.
• Familiarity with cloud services (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
• Ability to innovate and propose new solutions for clients.