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Summary
Cognizant, a global leader in professional services, is expanding its AI capabilities with a dedicated Gen AI Engineer role based in India. The position targets professionals who can design, train, and deploy advanced generative AI systems that combine text, image, and other data modalities.
Company : Cognizant
Position : Gen AI Engineer
Experience : Professional Experienced
Qualification : Graduate
Location : Gurgaon
Role overview
- Collaborate with cross-functional teams to collect, clean, and prepare data for building large-scale multimodal foundation models.
- Develop and optimize generative models such as GANs and VAEs, with a focus on language modeling, text generation, and contextual understanding.
- Regularly review and fine-tune large language models to ensure high accuracy for domain-specific datasets.
- Build, deploy, and manage AI applications on leading cloud platforms, including Azure, AWS, and GCP.
- Integrate AI models with existing enterprise data to enhance and augment current applications.
Key responsibilities
- Data preprocessing, augmentation, and synthetic data generation to strengthen model training.
- Design backend services using Python or .NET to support OpenAI-powered or other large-language-model solutions.
- Develop and maintain end-to-end AI pipelines, from data ingestion to model deployment.
- Work with custom datasets, employing techniques like chunking and embeddings for fine-tuning.
- Leverage cloud services and containerization (Docker, Kubernetes) to deploy scalable AI solutions.
- Align AI capabilities with business goals by collaborating with product and engineering teams.
- Stay current with the latest AI research and industry advancements to drive innovation.
Required skills and experience
- Strong foundation in machine learning, deep learning, and computer science principles.
- Proficiency with generative AI techniques, including GANs, VAEs, and transformer-based models.
- Experience in natural language processing (NLP) and a working understanding of computer vision is a plus.
- Proficiency in Python and AI-focused libraries such as TensorFlow, PyTorch, and Keras.
- Knowledge of NLP tasks including text generation, parsing, sentiment analysis, and transformer models (e.g., GPT variants).
- Experience with data management, preprocessing, augmentation, and synthetic data generation for model improvement.
- Experience deploying AI models in production environments and integrating them with enterprise data sources.
- Familiarity with cloud platforms (AWS, Azure, GCP) and containerization/orchestration tools like Docker and Kubernetes.
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