An excellent opportunity for Data Scientist/AIEngineer to be part of Cognizant’s Intelligent Process Automationpractice. It combines advisory services with deep vendorpartnerships and integrated solutions to create and executestrategic roadmaps. Key Responsibilities: - Imagine newapplications of generative AI to address business needs. -Integrate Generative AI into existing applications and workflows. -Collaborate with ML scientists and engineers to research, design,and develop cutting-edge generative AI algorithms to addressreal-world challenges. - Work across customer engagement tounderstand what adoption patterns for generative AI are working andrapidly share them across teams and leadership. - Interact withcustomers directly to understand the business problem, help and aidthem in the implementation of generative AI solutions, deliverbriefing and deep dive sessions to customers, and guide customerson adoption patterns and paths for generative AI. - Create anddeliver reusable technical assets that help to accelerate theadoption of generative AI on various platforms. - Create anddeliver best practice recommendations, tutorials, blog posts,sample code, and presentations adapted to technical, business, andexecutive stakeholders. - Provide customer and market feedback toProduct and Engineering teams to help define product direction. KeySkills and Experience: - Proficient in statistics, machinelearning, and deep learning concepts. - Skilled in Pythonframeworks such as scikit-learn, scipy, numpy, etc., and deeplearning libraries such as TensorFlow and Keras. - Experienced inGenAI projects such as text summarization and chatbot creationusing LLM models like GPT-4, Med-Palm, LLAMA, etc. - Skilled infine-tuning open-source LLM models such as LLAMA2 and Google Gemmamodel to 1-bit LLM using LORA, quantization, and QLORA techniques.- Skilled in RAG-based architecture using Langchain Framework andused Cohere model to fine-tune and re-rank the response ofGenAI-based chatbots. - Experience with image classification usingAI convolutional neural network models such as VGG 16, ResNet,AlexNet, and Darknet architectures in the computer vision domain. -Object detection using various frameworks such as YOLO, TFOD, andDetectron. - Knowledge in image classification, object detection,tracking, and segmentation. - Familiarity with neural networks,BERT, transformers, RAG, Langchain, prompt engineering, Azure AISearch, vector DB, and conversational AI, with LLMs used includingAzure OpenAI (GPT-4 Turbo), LLAMA2, Google Gemma, and Cohere model.#J-18808-Ljbffr
