Gen AI Developer

The Company India's marquee global technology & consulting company. They are an international flag-bearer of technical and managerial excellence. With offices around the globe, the company has a comprehensive presence across multiple segments of the technology product and service industries as well as a blue-chip roster of clients for their Consulting engagements. They are a respected career company and a long-term wealth creator. The Job We are seeking an experienced 3+ years of IT experience ( 2 Years in AI/ML/DS Domain). Gen AI Development : Data collection and preparation: Collect and prepare data for training and evaluating LLMs. This may involve cleaning and processing text data, or creating synthetic data. Model development: Design and implement LLM models. This may involve choosing the right architecture, training the model, and tuning the hyperparameters. Model evaluation: Evaluate the performance of LLM models. This may involve measuring the accuracy of the model on a held-out dataset, or assessing the quality of the generated text. Model deployment: Deploy LLM models to production. This may involve packaging the model, creating a REST API, and deploying the model to a cloud computing platform. Responsible AI: Should have proficient knowledge in Responsible AI and Data Privacy principles to ensure ethical data handling, transparency, and accountability in all stages of AI development. Must demonstrate a commitment to upholding privacy standards, mitigating bias, and fostering trust within data-driven initiatives. Experience in working with ML toolkit like R, NumPy, MatLab etc.. Experience in data mining, statistical analysis and data visualization Your Profile Roles and Responsibility: LLM Development/Deployment Pipeline creation, workflows Study and transform Gen AI PoCs to production grade deployment Design machine learning systems, Run machine learning tests and experiments Research and implement appropriate ML algorithms and tools Develop machine learning applications according to requirements Select appropriate datasets and data representation methods Perform statistical analysis and fine-tuning using test results Train and retrain systems when necessary