An excellent opportunity for 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.
The full job description covers all associated skills, previous experience, and any qualifications that applicants are expected to have.
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, help and aid them in the implementation of generative AI solutions, deliver briefing and deep dive sessions to customers, and guide customers on adoption patterns and paths 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, etc., and deep learning libraries such as TensorFlow and Keras.
* Experienced in GenAI 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 to 1-bit LLM using LORA, quantization, and QLORA techniques.
* Skilled in RAG-based architecture using Langchain Framework and used Cohere model to fine-tune and re-rank the response of GenAI-based chatbots.
* Experience with image classification using AI 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, and Detectron.
* Knowledge in image classification, object detection, tracking, and segmentation.
* Familiarity with neural networks, BERT, transformers, RAG, Langchain, prompt engineering, Azure AI Search, vector DB, and conversational AI, with LLMs used including Azure OpenAI (GPT-4 Turbo), LLAMA2, Google Gemma, and Cohere model.
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