Data Scientist/AI Engineer

An excellent opportunity for a Data Scientist/AI Engineer to be part of Cognizant’s Intelligent Process Automation practice. It combines advisory services with deep vendor partnerships and integrated solutions to create and execute strategic roadmaps.

Key Responsibilities:

• Imagine new applications 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 address real-world challenges.
• Work across customer engagement to understand what adoption patterns for generative AI are working and rapidly share them across teams and leadership.
• Interact with customers directly to understand the business problem, aid in the implementation of generative AI solutions, deliver briefings and deep dive sessions, and guide customers on adoption patterns for generative AI.
• Create and deliver reusable technical assets that help to accelerate the adoption of generative AI on various platforms.
• Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholders.
• Provide customer and market feedback to Product and Engineering teams to help define product direction.

Key Skills and Experience:

• Proficient in statistics, machine learning, and deep learning concepts.
• Skilled in Python frameworks such as scikit-learn, scipy, numpy, and deep learning libraries such as TensorFlow and Keras.
• Experience with generative AI projects such as text summarization and chatbot creation using LLM models like GPT-4, Med-Palm, LLAMA, etc.
• Skilled in fine-tuning open-source LLM models such as LLAMA2 and Google Gemma model using techniques like LORA, quantization, and QLORA.
• Experience with RAG-based architecture using the Langchain framework and using the Cohere model to fine-tune and re-rank responses of generative AI-based chatbots.
• Experience in image classification using AI convolutional neural network models such as VGG 16, ResNet, AlexNet, and Darknet architectures in the computer vision domain.
• Experience in object detection using various frameworks such as YOLO, TFOD, and Detectron.
• Knowledge of image classification, object detection, tracking, and segmentation.
• Familiarity with neural networks, BERT, transformers, RAG, Langchain, prompt engineering, Azure AI Search, vector databases, and conversational AI.
• Experience with LLMs including Azure OpenAI (GPT-4 turbo), LLAMA2, Google Gemma, and Cohere model.

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